Recently, cloud technology has become popular for smart societies. The Cloud technology has made dynamical network changes by enabling the construction of a logical network without building a physical network. Despite recent research on the cloud, it is necessary to study security functions for the identification of fake VNFs and the encryption of communication between entities. In this paper, we proposed an LW_PKI mechanism that detects a fake VNF and guarantees data security through mutual authentication between VNFs. To evaluate the LW_PKI, we built a MANO environment to test the performance of authentication and key generation for data security. In addition, we applied the artificial intelligence algorithm to detect abnormal behavior by using real attack data in the MANO environment. The LW_PKI guaranteed the reliability of a smart service by enhancing the security of the cloud environment.

Cloud computing transformed IT organizations with its on-demand model, notably Infrastructure as a Service (IaaS). Cloud providers manage extensive physical devices, consuming substantial energy and bandwidth for data traffic during virtual machine deployment. Balancing energy and bandwidth are critical. This thesis presents BOHGOA integrated with ACO to optimize virtual machine placement in clouds, simultaneously considering bandwidth and energy. It yields Pareto-optimal solutions for this trade-off. Using CloudSim, BOHGOA outperformed GA, ACO, and FFD algorithms, reducing bandwidth consumption by 54.14%, 32.11%, 57.47% (240 VMs) and 38.12%, 22.76%, 47.05% (500 VMs) respectively. Additionally, it decreased physical machine energy usage by 37.70%, 34.01%, 40.14% (240 VMs) and 27.50%, 22.28%, 30.52% (500 VMs) respectively. These results underscore BOHGOA's effectiveness in optimizing cloud VM placement.

Wireless Powered Communication Networks (WPCN), which has attracted much attention of researchers, also been recently recommended in 5^th generation (5G) wireless networks. With the help of the WPCN, the reliability and battery life of wireless low-power devices can be improved. In this paper, we investigate throughput and ergodic capacity in WPCN-assisted amplify-and-forward (AF) relaying system, considering two transmission modes including delay-tolerant and delay-limited. As important achievement, we propose symmetric energy harvesting protocol, namely time power switching relaying (TPSR) in order to find maximal throughput. In particular, both time switching and power switching coefficients in this schemes are considered. Unlike most of the previous works, we further focus on impact of outdated channel state information (CSI) in this WPCN. In order to evaluate information processing efficiency, the performance can be substantially improved by optimally harvesting time and power coefficients of the received signal at relay node for energy and information extraction, and by deploying several scenarios. By deploying Monte Carlo simulation, it is confirmed that the system performance is more sensitive to CSI estimation error, noise variance, signal-to-noise ratio (SNR) and resulting in other reasonable computations of TPSR need be deployed to obtain QoS requirement.

Agricultural greenhouse is largely answered in the agricultural sphere, despite the shortcomings it has, including overheating during the day and night cooling which sometimes results in the thermal inversion mainly due to its low inertia. The glasshouse dressed chapel is relatively more efficient than the conventional tunnel greenhouse. Its proliferation on the ground is more or less timid because of its relatively high cost[14-22]. Agricultural greenhouse aims to create a favorable microclimate to the requirements of growth and development of culture, from the surrounding weather conditions, produce according to the cropping calendars fruits, vegetables and flower species out of season and widely available along the year. It is defined by its structural and functional architecture, the quality thermal, mechanical and optical of its wall, with its sealing level and the technical and technological accompanying[12-13]. The greenhouse is a very confined environment, where multiple components are exchanged between key stakeholders and them factors are light, temperature and relative humidity[8]. This state of thermal evolution is the level sealing of the cover of its physical characteristics to be transparent to solar, absorbent and reflective of infrared radiation emitted by the enclosure where the solar radiation trapping effect otherwise called "greenhouse effect" and its technical and technological means of air that accompany. The socio-economic analysis of populations in the world leaves appear especially the last two decades of rapid and profound transformations These changes are accompanied by changes in eating habits, mainly characterized by rising consumption spread along the year[14]. To effectively meet this demand, greenhouse-systems have evolved, particularly towards greater control of production conditions (climate, irrigation, ventilation techniques, CO2 supply, etc ...). Technological progress has allowed the development of greenhouses so that they become increasingly sophisticated and of an industrial nature (heating, air conditioning, control, computer, regulation, etc ...). New climate driving techniques have emerged, including the use of control devices from the classic to the use of artificial intelligence[10-11] such as neural networks and / or fuzzy logic, etc... As a result, the greenhouse growers prefer these new technologies while optimizing the investment in the field to effectively meet the supply and demand of these fresh products cheaply and widely available throughout the year.

Distributed Energy Resources might have a severe influence on Power Line Communications, as they can generate interfering signals and high frequency emissions or supraharmonics that may cause loss of metering and control data. In this paper, the influence of various energy resources on Narrowband Power Line Communications is described and analyzed through several test measurements performed in a real microgrid. Accordingly, the paper describes the effects on smart metering communications through MAC layer analysis. Results show that the commutation frequency of inverters and the presence of battery chargers are remarkable sources of disturbance in low voltage distribution networks. In this sense, the results presented can contribute to efforts towards standardization and normative of emissions at higher frequencies higher, such as CENELEC EN 50160 and IEC/TS 62749.

In this paper, we look at how Artificial Swarm Intelligence can evolve using evolutionary algorithms that try to minimize the sensory surprise of the system. We will show how to apply the free-energy principle, borrowed from statistical physics, to quantitatively describe the optimization method (sensory surprise minimization), which can be used to support lifelong learning. We provide our ideas about how to combine this optimization method with evolutionary algorithms in order to boost the development of specialized Artificial Neural Networks, which define the proprioceptive configuration of particular robotic units that are part of a swarm. We consider how optimization of the free-energy can promote the homeostasis of the swarm system, i.e. ensures that the system remains within its sensory boundaries throughout its active lifetime. We will show how complex distributed cognitive systems can be build in the form of hierarchical modular system, which consists of specialized micro-intelligent agents connected through information channels. We will also consider the co-evolution of various robotic swarm units, which can result in development of proprioception and a comprehensive awareness of the properties of the environment. And finally, we will give a brief outline of how this system can be implemented in practice and of our progress in this area.

In this work we study the energy consumption by various modern secured hash functions (MD2, MD5, SHA-1, and SHA-2) and modern symmetric key encryption protocols (Blowfish, DES, 3DES, and AES) from the algorithmic perspective. We identify various parameters that moderate energy consumption of these hashes and protocols. Our work is directed towards redesigning or modifying these algorithms to make them consume lesser energy. As a first step, we try to determine the applicability of the asymptotic energy complexity model by Roy et. al. on these hashes and protocols. Specifically, we try to observe whether parallelizing the access of blocks of data in these algorithms reduces their energy consumption based on the energy model. Our results confirm the applicability of the energy model on these hashes and protocols. Our work is motivated by the relevance and importance of cryptographic hashes and symmetric key protocols for modern ICT (Information and Communication Technology), and ICT enabled industry to keep them protected from dynamically changing threat scenarios. Hence the design of more energy efficient hashes and protocols will definitely contribute in reducing the ICT energy consumption that is continuously increasing.

The building envelope has the most significant contribution in the reduction of the building energy consumption. Application of new, alternative and improved materials and systems has an important impact on the buildings performances. This paper is focused on the thermal transmittance, as an indicator of the thermal conductance of the construction element. It includes comparisons of the U-values, calculated by software, with those measured in situ on three representative façade walls. The walls have been constructed with the new wall system Fragmat NZ-1, a new product in Macedonian buildings. This research provides basic information on the thermal transmittance of the system. The results of the analysis show that the in situ measuring is a useful tool in validation of the precision of analytically calculated values, since it was difficult to obtain precise results from the analytical calculations only, especially when the layers are with non-uniform thickness.

This review covers some of the central relationships between artificial intelligence, psychology, and economics through the lens of information theory, specifically focusing on formal models of decision-theory. In doing so we look at a particular approach that each field has adopted and how information theory has informed the development of the ideas of each field. A key theme is expected utility theory, its connection to information theory, and the Bayesian approach to decision-making and forms of (bounded) rationality. What emerges from this review is a broadly unified formal perspective derived from three very different starting points that reflect the unique principles of each field. Each of the three approaches reviewed can, in principle at least, be implemented in a computational model in such a way that, with sufficient computational power, they could be compared with human abilities in complex tasks. However, a central critique that can be applied to all three approaches was first put forward by Savage in {\it The Foundations of Statistics} and recently brought to the fore by the economist Binmore: Bayesian approaches to decision-making work in what Savage called `small worlds' but cannot work in `large worlds'. This point, in various different guises, is central to some of the current debates about the power of artificial intelligence and its relationship to human-like learning and decision-making. Recent work on artificial intelligence has gone some way to bridging this gap but significant questions still need to be answered in all three fields in order to make progress on these problems.

: The significance of heating load (HL) accurate approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are through synthesizing multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis.

Water covers a greater part of the earth's surface. Even though we know very little about the underwater world as most parts of it remain unexplored. Oceans including other water bodies hold huge natural resources and also the aquatic lives. These are mostly unexplored and very few of those are known due to unsuited and hazardous environments for the human to explore. This vast underwater world can be monitored remotely from a distant location with much ease and less risk. To monitor water-bodies remotely in real-time, sensor networking has been playing a great role. It is needed to deploy a wireless sensor network over the volume which we want to surveil. For vast water bodies like oceans, rivers and large lakes, data is collected from the different heights of the water level which is sent to the surface sink. Unlike terrestrial communication, radio waves and other conventional mediums can't serve the purpose of underwater communication as they pose high attenuation and very reduced transmission range. Rather an acoustic medium can transmit data more efficiently and reliably in comparison to other mediums. To transmit data reliably from the bottom of the sea to the sinks at the surface, multi-hop communication is needed which must involve a certain scheme. For seabed to surface sink communication, leading researchers have proposed different routing protocols. The goal of these routing protocols is to make underwater communication more reliable, energy-efficient and delay efficient thus to improve the performance of the overall communication. This paper surveys the advancement and applications of the routing protocols which eventually helps in finding the most efficient routing protocol for the Underwater Wireless Sensor Network (UWSN).

This article proposes a particular strategy to proceed with a progressive electrification of public transport systems in cities. Starting from a bus operation model, the possible electrification of two routes is analysed, one urban and another extra-urban in the city of Pisa. An estimate is made of the energy uses associated with certain operating modes. The maximum level of consumption is estimated at approximately 280 kWh per day per bus for the urban route and excluding some special days, less than 215 kWh per day for the extra-urban route, for which a hybrid bus is proposed. Starting from an estimate of the daily consumption for the management of the two routes, the sizing of a photovoltaic system distributed on some modular shelters which serves to power the same routes is carried out. The resulting system has a power of the order of 190-200 kW. The modular solution is also outlined, and an installation is proposed. The analyzed case lends itself to being easily replicated.

Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimising MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based-control and deep learning techniques, including gated recurrent units (GRU), basic recurrent neural network (RNN) and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimises MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyper-parameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an R2 value close to 1, representing a strong correlation between predicted and actual power values.

The article presents a bibliometric analysis of conference proceedings 2019-2021 indexed in Scopus and related to the topic "Artificial Intelligence and Energy". The relevance of the topic is considered in the context of the energy transition and the application of artificial intelligence for the rational use of energy resources. A comparative analysis of the publication activity of researchers from Russia, China, India, the United States, and Indonesia on the topic in question is given. Methodologically, the emphasis for determining the subject is not on the co-occurrence of keywords, but on the clustering of documents based on the thesis that the titles of publications most briefly and fully reveal the content of the publication and allow the subject specialist to do a relevant selection of publications necessary for their research. Two tools were used in this work: Carrot2 and Clustering App (CSV Explorer). It is shown that Russian researches are characterized by the tendency of prevailing engineering problems and insufficient attention is paid to environmental, economic and social problems of using AI in the energy sector. Studies from China and the United States are of the greatest informative interest, both in terms of the relevance of their topics and the actuality of their content. Studies from India and Indonesia are more general in nature; of interest are the efforts of these countries to engage in global scientific activities.

Compressed carbon dioxide energy storage (CCES) offers several benefits over other existing energy storage systems, including ease of liquefaction, high energy storage density, and environmental friendliness. As a result, the research progress, economic and technological feasibility, and system operation of the CCES system are all discussed in depth in this study. The system evaluation method is summarized and the compressed carbon dioxide storage is analyzed, and the performance optimization direction of the compressed carbon dioxide energy storage technology is discussed. When the overall performance of a transcritical CCES system, a supercritical CCES system and a liquid CCES system are compared, it is discovered that the supercritical CCES system has better thermodynamic characteristics and a simpler system configuration, making it suitable for large-scale development and use. The goal of the CCES system's future development is to create a design with an optimum compression and expansion ratio, a more precise analysis and system model, and multi-field coupling. This review's discussion serves as a guide for the best design and use of the CCES system.

To protect power lines, distance relays are used which can rely on a modified distance resistance. But usually the operating range of these relays changes as the network conditions change (network topology, load value, output value, etc.) and leads to false trips, By using methods that can process information and recognize patterns, for example, the use of micro and intelligent processor algorithms can use the new relays with high precision and thus provide adequate protection. In this study, a distance relay was modeled using a neural network, and it was observed that the neural relay had higher accuracy than the conventional relay. In addition to detecting the fault and its location, type and phase of the fault, three-stage simultaneous protection can be performed. As a result, the number of linear relays can be reduced by using relays based on neural technologies. An MLP (multilayer perceptron) neural network is used to model a sequence of distances.

This paper presents the implementation of a control strategy based on fuzzy logic artificial intelligence (AI) for speed regulation of an electromagnetic frequency regulator (EFR) prototype, aiming to eliminate the dependence on knowledge of physical parameters in the most diverse operating conditions. Speed multiplication is one of the most important steps in power generation wind. Gearboxes are generally used for this purpose. However, they have a reduced lifespan and a high failure rate and are still noise sources. The search for new ways to match the speed (and torque) between the turbine and the generator is an important research area to increase the energy, financial and environmental efficiency of wind systems. The EFR device is an example of an alternative technology that this team of researchers has proposed. It counts the fact of taking advantage of the main advantages of an induction machine with the rotor in a squirrel cage positively. In the first studies, the EFR control strategy consisted of the conventional PID controllers, which has several limitations widely discussed in the literature. This strategy also limits the EFR's performance, considering its entire operating range. The simulation program was developed using the Matlab/Simulink platform, while the experimental results were obtained in the laboratory emulating the EFR-based system. The EFR prototype used has 2 poles, a nominal power of 2.2 kW, and a nominal frequency of 60 Hz. Experimental results were presented to validate the efficiency of the proposed control strategy.

This paper focused on the finite element analysis of structural system in extreme loading condition. Two different stud shape and thicknesses were analyzed under blast. The stud thickness such as 1.19 mm and 1.5 mm were modelled and analyzed using ABAQUS 6.14. FEM is a tool which predicts the engineering physics of the real structure. To validate the finite element modelling performed by authors, a reference work published by earlier researchers on cold-formed steel stud wall is considered and examined in the present study. The novelty of this study was web corrugation and influence of flange width on stud. The models mimic like an air bag in a car to delay the pressure timing inside the stud wall. The mass of explosive used as 1.56 kg at a standard scaled distance. Time versus displacement was captured out at four locations in the stud wall. One of the objectives is to develop mathematical model to validate the deformation of stud under blast loading. Two mathematical models were validated using Artificial Neural Network (ANN). The results captured in ANN model was error histogram, regression plot, best performance fit and training data. The models were capable of resisting the moderate blast load. The response surface methodology (RSM) was employed to evaluate model performance Regression equations are useful for predicting future trends and outcomes, which is crucial for planning and decision-making. The primary goal of this work is to evaluate cold-formed steel stud walls with varying stud sizes under blast loading using finite element analysis and validated by ANN and RSM.

Abstract: The COVID-19 pandemic has disrupted global energy markets and caused significant socio-economic impacts worldwide. The pandemic has also impacted the energy sector, with demand for energy reducing due to lockdowns and reduced economic activity. Hence, this paper aims to provide a comprehensive and analytical review of the impact of COVID-19 on the energy sector and discuss the potential role of artificial intelligence (AI) in mitigating some of these effects. This review examines the changes in energy demand patterns resulting from the pandemic and the implications for the energy industry, including the shifts in policymaking, communication, digital technology, energy conversion, environmental, energy market, and power system operation. The analysis of the energy pattern is discussed according to the timeframes which include pre-, mid-, and post-pandemic periods. Furthermore, we explore how AI can be used to improve energy efficiency, optimize energy use, and reduce energy wastages. The potential for AI to contribute to developing more efficient and sustainable energy systems has also been addressed. Lastly, we highlighted AI’s challenges, which play a significant role in the energy sector's response to the COVID-19 pandemic. The recommendations for AI applications in the energy sector for the transition to a more sustainable energy future are outlined. Information corroborated in this review is expected to provide important guidelines for crafting future research areas and directions in preparing the energy sector for any unforeseen circumstances or pandemic-like situations.

In today's world, the significance of reducing energy consumption globally is increasing, making it imperative to prioritize energy efficiency in the 5th Generation (5G) networks. However, it is crucial to ensure that these energy-saving measures do not compromise the Key Performance Indicators (KPIs) such as user experience, quality of service (QoS), or other important aspects of the network. To address this challenge, advanced wireless technologies have been incorporated into the design of 5G networks across various network layers. Given the rapid advancements in Artificial Intelligence (AI) and emerging technology trends such as Machine Learning (ML), which is considered a subset of AI, the integration of such trends into 5G networks has become a significant area of research. The primary objective of this survey is to analyze AI integration into 5G networks for enhanced energy efficiency. By exploring this intersection between AI and 5G, we aim to identify potential strategies and techniques for optimizing energy consumption while maintaining the desired network performance and user experience.

Wireless Sensor Networks (WSNs) continue to provide essential services for various applications such as surveillance, data gathering, and data transmission from the hazardous environments to safer destinations. This has been enhanced by the energy-efficient routing protocols that are mostly designed for such purposes. Gateway-based Energy-Aware Multi-hop Routing protocol (MGEAR) is one of the homogenous routing schemes that was recently designed to more efficiently reduce the energy consumption of distant nodes. However, it has been found that the protocol has a high energy consumption rate, lower stability period, less data transmission to the Base station (BS). In this paper, an enhanced Heterogeneous Gateway-based Energy-Aware multi-hop routing protocol ( HMGEAR) is proposed. The proposed routing scheme is based on the introduction of heterogeneous nodes in the existing scheme, selection of the head based on the residual energy, introduction of multi-hop communication strategy in all the regions of the network, and implementation of energy hole elimination technique. Results show that the proposed routing scheme outperforms two existing ones.

Utilization of wasted heat instead of fuel combustion is effective to reduce primary energy consumption for mitigating global warming problem. Because wasted heat sources are not necessarily located close to areas of heat demand, one of the difficulties is that wasted heat has to be transferred from heat source side to heat demand side, which may require heat transportation over long distance. From this point we proposed and have examined new idea of heat transportation using ammonia-water as the working fluid which system is named Solution Transportation Absorption chiller, in short STA. Our previous studies of STA were mainly the experimental investigation with STA facility which cooling power was 25RT (90kW). As a result, the COP of STA was found almost same value 0.65 with the conventional absorption chiller without depending on the transportation distances. The simulation using AspenHYSYS also examined with same experimental condition. The experimental data showed good agreement with the simulation calculation. In this study, we examined the large-scale cooling power STA on simulation. The examination cooling powers were from 90 kW(25RT) to 3517 kW(1000RT). All cooling power achieved around COP 0.64 including pump power consumptions. In addition, we performed the dynamic simulation. As the results, there was no effect of pipeline size on the cooling capacities and mass flow rates. Furthermore, the stability time of the cooling capacities and mass flow rates were almost same regardless of the pipeline size and cooling capacity. In other words, STA may be achieved the same COP even though having various complex conditions compared with the conventional absorption chiller.

The quickest and easiest way to avoid greenhouse gas (GHG) emissions is to purchase renewable electricity and offset the remaining emissions. However, the industrial sector’s electricity needs already exceed renewable electricity generation. Moreover, electricity accounts for only one third of the industry’s energy needs. Simultaneously, the advance of sectoral coupling and the decarbonisation of industrial processes, as well as the desire to rapidly decrease dependence on fossil fuels, are creating significant additional demand for renewable energy. Neither existing nor planned generation and transmission infrastructure will suffice to meet the expected short-term demand. Based on survey data from the German Industry Energy Efficiency Index, this article therefore examines the share of GHG savings that companies intend to achieve on- and off-site. Understanding how much additional generation and transmission capacity is needed by the industry to decarbonise and by when is crucial to identify and address the extent of excess demand. On average, companies plan to avoid 22 % of their 2019 emissions by 2025 and 27 % by 2030, primarily through on-site measures. In combination with the extrapolation of the entire industry’s needs for off-site capacity, the data calls for a rapid expansion of planning authority and green generation capacities.

Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings' energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this work is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS-ANN). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA), are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model's optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90 % correlation) can adequately optimize the ANN. In this regard, this tool's prediction error declined by nearly 23, 18, and 36 % by applying the GOA, FA, and SFS techniques. Also, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL.

In this study, a novel two-layer control scheme aimed at enhancing the dynamic performance of a nonlinear vehicle through the utilization of an optimal control approach grounded in predictive control principles is presented. The first layer of our control system focuses on the computation of an optimized rotational torque that ensures lateral dynamic stability. This torque is subsequently translated into differential forces acting on the wheels, employing the inverse tire model to derive the desired longitudinal slips. These slip values are then transmitted to the second layer of our control system. In the second layer, the electric motors associated with the vehicle's wheels dynamically adjust the input torque to accurately track the desired slip, thereby ensuring the overall stability of the electric vehicle. In view of the paramount importance of energy consumption in electric vehicles, we adopt optimal control strategies to substantially minimize battery utilization. To this end, careful selection of appropriate weighting coefficients in the control laws enables us to maintain the electric motors within their permissible operational range while simultaneously minimizing battery energy consumption for desired slip tracking. Extensive simulation results validate the effectiveness of our proposed control system in proficiently managing nonlinear effects and safeguarding the vehicle's stability.

Green innovation has become an important combination of high-quality economic growth and sustainable development of ecological environment. In this paper, the super-efficiency network SBM model is used to measure the two-stage green innovation efficiency of industrial science and technology R&D and achievement transformation in 30 provinces and cities from 2009 to 2019, and exploratory Data Analysis (ESDA) and spatial econometric model are used to investigate the spatial-temporal evolution characteristics and influencing factors of green innovation efficiency. The results show that: firstly, the overall efficiency of industrial green innovation is low, and the efficiency of scientific research and development and achievement transformation has experienced three stages of "upward-declining-revitalized period". The low efficiency of achievement transformation is an important factor hiding the improvement of the efficiency of industrial green innovation. Secondly, The industrial green innovation efficiency gradually increases from northwest to southeast, forming a centralized "line" and "block" distribution. The high efficiency area is still concentrated in the eastern coastal region, and the balanced development trend is obvious in the central and western regions. Finally, openness has a positive impact on the two-stage green innovation efficiency; Industrial structure and government investment in science and technology have a positive impact on the efficiency of science and technology research and development, but have no significant effect on the efficiency of achievement transformation. Enterprise size has a positive effect on achievement transformation efficiency, but has no significant effect on R&D efficiency. Environmental regulation has a positive impact on R&D efficiency and a negative impact on achievement transformation efficiency.

The integration of the increasing share of Renewable Energy Sources (RES) requires the availability of suitable energy storage systems to improve the grid flexibility, and Compressed Air Energy Storage (CAES) systems could be a promising option. In this study, a CO2-free Diabatic CAES system is proposed and analysed. The plant configuration is derived from a down-scaled version of the McIntosh diabatic CAES plant, where the natural gas is replaced with green hydrogen, produced on site by a Proton Exchange Membrane electrolyser powered by a Photovoltaic power plant. In this study, the components of the hydrogen production system are sized to maximize the Self-Consumption share of PV energy generation and the effect of the design parameters on the H2-CAES plant performance are analysed on a yearly basis. Moreover, a comparison between the use of natural gas and hydrogen in terms of energy consumption and CO2 emissions is discussed. The results show that the proposed hydrogen fuelled CAES can effectively match the generation profile and the yearly production of the natural gas fuelled plant by using all the PV energy production, while producing zero CO2 emissions.

Internet of things (IoT) is a network of smart things. This indicates the ability of these physical things to transfer information with other physical things. The characteristics of these networks, such as topology dynamicity and energy constraint, challenges the routing problem in these networks. Previous routing methods could not achieve the required performance in this type of network. Therefore, developers of this network designed and developed specific methods in order to satisfy the requirements of these networks. One of the routing methods is utilization of multipath protocols which send data to its destination using routes with separate links. One of such protocols is RPL routing protocol. In this paper, this method is improved using composite metrics which chooses the best paths used for separate routes to send packets. We propose Energy and Load aware RPL (ELaM-IoT) protocol, which is an enhancement of RPL protocol. It uses a composite metric, calculated based on remaining energy, hop count, Link Expiration Time (LET), load and battery depletion index (BDI) for the route selection. In order to evaluate and report the results, the proposed ELaM-IoT method is compared to the ERGID and ADRM-IoT approaches with regard to average remaining energy, and network lifetime. The results demonstrate the superior performance of the proposed ELaM-IoT compared to the ERGID and ADRM-IoT approaches.

Driven by the proliferation of DC energy sources and DC end-use devices (e.g., photovoltaics, battery storage, solid-state lighting, and consumer electronics), DC power distribution in buildings has recently emerged as a path to improved efficiency, resilience, and cost savings in the transitioning building sector. Despite these important benefits, there are several technological and market barriers impeding the development of DC distribution, which have kept this technology at the demonstration phase. This paper identifies specific end-use cases for which DC distribution in buildings is viable today. We evaluate their technology and market readiness, as well as their efficiency, cost, and resiliency benefits while addressing implementation barriers. The paper starts with a technology review, followed by a comprehensive market assessment, in which we analyze DC distribution field deployments and their end-use characteristics. We also conduct a survey of DC power and building professionals through on-site visits and phone interviews and summarize lessons learned and recommendations. In addition, the paper includes a novel efficiency analysis, in which we quantify energy savings from DC distribution for different end-use categories. Based on our findings, we present specific adoption pathways for DC in buildings that can be implemented today, and for each pathway we identify challenges and offer recommendations for the research and building community.

Ninety percent of the existing building stock in Europe was built before 1990. These buildings are in urgent need for a significant improvement of energy-efficiency through renovation. Regrettably, so far only five percent of renovation projects have been able to yield energy-saving at deep renovation level. State-of-the-art renovation solutions are available, but costly and lengthy renovation processes and incomprehensible technical complexities hinder the achievement of a wide impact at a European scale. This paper presents research on Plug-and-Play (PnP) technologies supported by Building Information Modelling (BIM) to provide affordable, interchangeable and quick-installation solutions to overcome the main barriers of building deep renovation.

Dealing with and maintaining high-quality standards in the design and construction phases is challenging, especially for on-site construction. Issues like improper implementation of building components and poor communication can widen the gap between design specifications and actual conditions. To prevent this, particularly for energy-efficient buildings, it's vital to develop resilient, sustainable strategies. These should optimize resource use, minimize environmental impact, and enhance livability, contributing to carbon neutrality by 2050 and climate change mitigation. Traditional post-occupancy evaluations, which identify defects after construction, are impractical for addressing energy performance gaps. A new, real-time inspection approach is necessary throughout the construction process. This paper suggests an innovative guideline for prefabricated buildings, emphasizing digital 'self-instruction' and 'self-inspection'. These procedures ensure activities impacting quality adhere to specific instructions, drawings, and 3D models, incorporating relevant acceptance criteria to verify completion. This methodology, promoting alignment with planned energy-efficient features, is supported by BIM-based software and Augmented Reality (AR) tools, embodying Industry 4.0 principles. BIM and AR bridge the gap between virtual design and actual construction, improving stakeholder communication and enabling real-time monitoring and adjustments. This integration fosters accuracy and efficiency, key for energy-efficient and Nearly Zero-Energy Buildings, marking a shift towards a more precise, collaborative, and environmentally sensible construction industry.

The IPCC’s sixth assessment report projects 15% to 43% (44 EJ/y – 310 EJ/y) of global primary energy to be generated by biomass in 2050 across multiple GHG mitigation scenarios. That report also emphasises the importance of electrification to meet GHG reduction targets. With increased reliance on electric power, and increased appeal to biomass, bioenergy for electricity is expected to play a major role in future energy markets. What makes the bioenergy solution more attractive is its reported reasonable Energy Return on Investment (EROI). However, generation at large scale is projected to be greatly dependent on crops and plantations. This shifts the GHG emissions concern to be concerns over land use and other emissions integrated in the bioenergy lifecycle. It is therefore vital to know whether the potential of electricity generation from biomass outweighs environmental impact of bioenergy. This paper evaluates the potential of biomass electricity mainly generated from short rotation woody crops combustion in generating green energy. This is done using the “Green EROI (EROIg)” quantification methodology, which indicates the net energy generated to society after investing in ecosystem maintenance energy (ESME). ESME is a non-monetary weighting mechanism of an entity’s different lifecycle environmental impacts. This study found that the EROIg of bioelectricity is marginally larger than unity when converted to its primary equivalent form (EROIg-PE) which indicates that the technology is somewhat energetically viable if its production was to be green. Three design options were proposed to improve bioenergy’s EROIg performance, these include adding 20% waste wood in the combustion mix, staggered harvesting and plantation to achieve annual harvest and pelletizing wood. This approach appeared to improve the EROIg especially for pelletizing, due to its simultaneous reduction in storage and transport costs, making the production energetically and environmentally viable even at a 1 : 1 secondary : primary ratio with an EROIg of 1.11 and an EROIg-PE of 3.17. We conclude with the discussion of the multiple indirect advantages of growing crops that can be used for energy generation, and a discussion on how this technique can be used alongside others to help them generate cleaner energy while facing the current global climate, biodiversity and waste issues.

Resource pricing in cloud computing has become one of the main challenges for cloud providers. The challenge is determining a fair and appropriate price to satisfy users and resource providers. To determine a fair price, it is necessary to consider the conditions and needs of both parties: provider and user. This research tries to provide a pricing mechanism for cloud computing based on game theory. The proposed mechanism takes into account three factors including fault possibility, interaction between virtual machines, and energy consumption to reach a fair price. In the proposed game, the provider decides on the price of the virtual machine which can be offered to the user on each physical machine and the user decides between the virtual machines offered to run its application. The whole game is implemented as a function of the resource broker component. The proposed mechanism is simulated and evaluated using the CloudSim simulator. Its performance is compared with several previous recent mechanisms. According to the results, the proposed mechanism has been able to determine a more reasonable price for the user and the provider which increases the overall profitability of the cloud system.

As urbanization continues to accelerate, the concept of smart buildings has transitioned from a futuristic vision to an integral component of modern cities. These buildings, often seen as the building blocks of smart cities, are increasingly relying on cutting-edge technologies like 5G and cloud computing to reach unprecedented levels of energy efficiency, sustainability, and occupant comfort. This paper offers a comprehensive exploration into the marriage of smart building technologies with 5G and cloud computing. It scrutinizes the individual and combined impacts of these technologies, presents various case studies showcasing real-world applications, and anticipates future trends. Moreover, it delves into technical challenges, policy implications, and offers concrete recommendations for stakeholders.

This paper explores the latest developments in electrolysis technology, a key player in the transition to sustainable energy systems. Electrolysis, despite currently contributing a small share to global hydrogen production, holds immense potential for producing green hydrogen. The study delves into the efficiency of electrolysis systems, emphasizing ongoing efforts to enhance energy conversion rates. It investigates the impact of high-temperature electrolysis on reducing electricity consumption, thus making the process more efficient. The paper discusses the various challenges in the research on water electrolysis and underscores the critical role electrolysis plays in integrating renewable energy sources. The study emphasizes the need for continuous advancements in electrolysis technology to bridge existing gaps, making a compelling case for its pivotal role in the green hydrogen revolution.

WBAN is a blessing to mankind because it can monitor people's health and activities regardless of place or time. It brings feasibility in patients’ treatment. The main problems with WBAN, however, are related to the security and privacy of health data. To prevent data misuse, health information should be safeguarded. On the other hand, the medical professional needs to receive the patient's health information promptly. As security of health data impact human life greatly so secure an efficient method is required. Efficient cryptographic technique for data protection of wireless body area network is proposed. This research aims to provide data protection in WBAN to increase data security and efficient transmission. The suggested approach offers effective security with quick key generation, quick encryption, and secure data transmission across the network. The proposed methodology is considered to be secure and will consume less time. The proposed method further uses a less complex encryption algorithm. The less complex the approach is the higher performance it will provide in wireless network. The proposed methodology will prevent many attacks, which makes the proposed approach highly suitable for wireless body area network’s security.

Solar green roofs, namely rooftops functionalized with properly selected living vegetation and photovoltaic modules, achieve an ideal symbiotic relationship in which promotion of biodiversity and onsite renewable energy production are both enhanced whereas the roof provides a wide range of environmental, health, aesthetic and economic benefits. This study provides a unified outlook of this eminent sustainable technology at the dawn of its uptake across the world, especially in polluted urban areas.

The energy transition to wind and solar opens opportunities for green hydrogen as the wind and solar generation tends to bring electricity prices down to very low levels. This paper evaluates whether green hydrogen can integrate well with wind and solar PV to help with improved management of the South Australian electricity grid. Green hydrogen can use membrane electrolysis plants during surplus renewable energy periods. This hydrogen can then be electrified or used in industry. The green hydrogen system was analysed to understand the financial viability and technical impact of integrating green hydrogen. We also used systems engineering techniques to understand the system holistically, including the technical, social, environmental, and economic impacts. The results show opportunities for the system to provide seasonal storage, grid firming, and reliability services. Financially, it would need changes to electricity rules to be viable, but without those changes, our modelling suggested it would be financially viable within this decade

This study investigates the relationship between energy metals and precious metals to assess their suitability as safe haven assets in clean energy investment portfolios. It focuses on the impact of events in 2020 and 2022, characterized by substantial investments in clean energy. The research reveals that, except for Nickel Futures (NICKELc1), energy metals are positively linked with clean energy indexes. This suggests that they can serve as a secure investment option for green investors looking to diversify their portfolios. As a result, the study dismisses the initial question, indicating that energy metals can indeed be considered safe havens within the context of clean energy investments. Additionally, the research reinforces prior findings that precious metals like gold, silver, and platinum possess safe haven characteristics in relation to specific clean energy stock indexes. These results have significant implications, particularly given the increasing investments in clean energy stocks and the recurring uncertainties in the market. In summary, this study supports the idea that both energy metals and precious metals can play valuable roles in clean energy portfolios, providing stability during turbulent times in the market.

The present article analyzes the integration of green building policy and practice with the largest low-income housing production program in the US and the innovativeness of its housing agencies. Drawing on policy innovation literature, panel data and regression analysis are employed to quantify associations between state-level characteristics and the adoption of green building criteria into the Low-Income Housing Tax Credit (LIHTC) program. Results show that, on average, housing agencies have increasingly adopted green building criteria, and most have identified co-benefits from energy-efficient buildings and smart growth. Despite overall progress, the rate of adoption of green building criteria has decreased, few states have comprehensive criteria, and many have dropped important criteria, such as on-site renewable energy generation. Results are consistent with hypotheses derived from the literature and suggest the integration of green building with LIHTC developments is significantly associated with government motivation, financial resources, and exogenous characteristics that affect the demand for green building. Future research should explore organization-level factors that affect environmental policy innovation. It is recommended that LIHTC housing agencies require compliance with green building rating systems and periodically reconfigure green building criteria based on planned evolutionary change, data-driven strategies, and life-cycle analyses towards zero net energy consumption.

The rapid development of China's economy has led to a rapid increase in energy production and use. Among them, the excessive consumption of coal in fossil energy consumption is the leading cause of air pollution in China. This paper incorporates renewable energy innovation, fossil energy consumption and air pollution into a unified analysis framework, and uses spatial measurement models to investigate the spatial effects of renewable energy green innovation and fossil energy consumption on air pollution in China, and decomposes the total impact into direct and indirect effects. influences. The empirical results show that China's air pollution, renewable energy green innovation and fossil energy consumption are extremely uneven in geographical space, generally showing the characteristics of high in the east and low in the west, and showing a strong spatial aggregation phenomenon. Fossil energy consumption will lead to increased air pollution, and the replacement of fossil fuels with clean and renewable energy is an important means of controlling pollution emissions. The direct and indirect effects of renewable energy green innovation on air pollution are significantly negative, indicating that renewable energy green innovation not only suppresses local air pollution, but also suppresses air pollution in neighboring areas. The consumption of fossil energy will significantly increase the local air pollution, and the impact on the SO2 and Dust&Smoke pollution in the adjacent area is not very obvious. It is recommended to strengthen investment in renewable energy green innovation, reduce the proportion of traditional fossil energy consumption, and pay attention to the spatial connection and spillover of renewable energy green innovation.

Drops are the most important and most common energy dissipator in irrigation networks and erodible canals and consequently, their performance must be well understood. This study was designed to evaluate the capability of Artificial Intelligence (AI) methods including ANN, ANFIS, GRNN, SVM, GP, MLR, and LR to predict the relative energy dissipation (∆E/E0) in vertical drops equipped with a horizontal screen. For this study, 108 experiments were carried out to investigate energy dissipation with variable discharge, varying drop height, and porosity of the horizontal screens. Parameters yc/h, yd/yc, and p are considered as input variables and ∆E/E0 is the output variable. The efficiency of models was compared using Taylor's diagram, Box Plot of the applied error distribution, correlation coefficient (CC), mean absolute error (MAE) and root-mean-square error (RMSE). Results indicate that the performance of the ANFIS_gbellmf based model with CC value of 0.9953, RMSE value of 0.0069 and MAE value of 0.0042 was superior to other applied models. Also, the linear regression model with CC=0.9933, RMSE=0.0083, and MAE= 0.0067performs better than the multiple linear regression model in this study. Results of a sensitivity study suggest that yc/h is the most effective parameter for predicting ∆E/E0.

The recent advancements in the field of communication have led data sharing to become an integral part of today's smart cities with the evolution of concepts such as the internet of vehicles (IoV) paradigm. As a part of IoV, Electric Vehicles (EVs) have recently gained momentum as authorities have started expanding their Low Emission Zones (LEZ) in an effort to build green cities with low carbon footprints. Energy is one of the key requirements of EVs not only to support the smooth and sustainable operation of EV itself but to also ensure connectivity between the vehicles and infrastructure with controlling devices like sensors and actuators installed within an EV. In this context, renewable energy sources (such as wind energy) dramatically play their parts in the automobile sector towards designing the energy harvesting electric vehicles (EH-EV) to pare the energy reliance on the national grid. In this article, a novel approach is presented to achieve electric generation due to vehicle mobility to support the communication primitives in electric vehicles which enables plenty of IoV use cases in the presence of surplus energy at hand. A small-scale wind turbine is designed to harness wind power for converting it into mechanical power. This power is then fed to the onboard DC generator to produce electrical energy. Furthermore, the acquired power is processed through a regulation circuitry to consequently achieve the desired power supply for the end load, i.e. the batteries installed. The suitable orientation for efficient power generation is proposed on ANSYS-based aerodynamics analysis. The voltages induced by DC generator at No-Load condition are 35V while at Full-Load 25V are generated at rated current of 6.9A, along with the generation of power at around 100W (at constant voltage) at the rated speed of 90 mph for nominal battery charging. Moreover, the acquired data can be monitored via an android application interface by using a Bluetooth module.

Real assets in the energy market are subject to ecological uncertainty due to the penetration of renewables. We illustrate this point by analysing electrolysers, a class of assets which recently became the subject of a large interest, as they lead to the production of the desirable green hydrogen and green ammonia. The latter has the advantage of being easily stored and has huge potential in decarbonising both the fertilizer and shipping industries. We consider the optimization of green ammonia production with different types of electricity procurement in the context of stochastic power and ammonia markets, a necessary assumption to translate the features of renewable, hence intermittent, electricity. We emphasise the importance of using stochastic prices to effectively model the volatile nature of the price dynamics, illustrating the project risks that can be mitigated by hedging activities. This study shows the pivotal role of flexibility when dealing with fluctuating renewable production and volatile electricity prices to maximise profits and better manage risks.

This study aimed to increase the energy independence of buildings by utilizing solar energy to produce renewable energy. The subject of this study was a shading structure installed in an outdoor space to provide solar energy. Solar panels were applied to the shaded structures to generate energy actively. The solar panels were designed to be moved according to the optimal tilt angle each month to produce the optimal renewable energy. The architectural design of the shading structure and the energy simulation of the solar panels were conducted using a parametric design. The results of the energy simulation showed the generation of 31,570 kWh· year−1 of renewable energy. This amount of energy is 10% higher than that produced by fixed solar panels. Thus, the moving solar panel system developed in this study not only increases the energy independence of buildings, but also has the advantage of higher renewable energy production compared with fixed solar panels. Additionally, various types of shading structures can be designed depending on the combination of solar panel modules; in particular, moving solar panels have the potential to facilitate ecofriendly designs when applied to the exterior of buildings.

The rising global concern for environmental sustainability has driven a surge in green invest-ments' popularity. This study examines the multifractal characteristics and efficiency of various green stock indexes, such as Clean Science and Technology, ISE Clean Edge Global Wind Energy Index, iShares Global Clean Energy ETF, Nasdaq Clean Edge Green Energy, and Solactive Clean Energy. The research covers the period from October 4, 2021, to October 4, 2023. The central ob-jective of this research is to evaluate the efficiency of clean energy stock markets and dissect their influence on investment decisions and market performance in the realm of green investments. To accomplish this, the study employs the Wright test (2000) and the Detrended Fluctuation Analysis (DFA) method. The findings indicate the presence of negative serial autocorrelation within the Rankings and Signals tests. Furthermore, the DFA analysis concurrently reveals long memory patterns in most green indexes, with the notable exception being the Nasdaq Clean Edge index, which exhibits tendencies toward anti-persistence. These results imply that prices inadequately incorporate available information, resulting in non-i.i.d. price fluctuations. These implications are significant for investors, as they indicate the potential for predictable returns and opportunities for arbitrage and abnormal profits, challenging the assumptions of random walk and information efficiency hypotheses.

This comprehensive critical review shows how green buildings and energy optimization solutions improve indoor air quality as well as environmental quality (IEQ) and human health. Electricity-efficient green buildings create a better indoor atmosphere and save electricity. Green buildings improve occupant well-being through IAQ, thermal comfort, acoustics, and lighting. Renewable energy, green construction materials, and intelligent technologies increase sustainability and reduce environmental impact. Environmental hazards, architectural design, and societal variables can improve indoor air quality and subsequently reduce health risks linked with poor air quality. The evaluation highlights the pros and cons of low-cost IAQ sensors and equipment. These devices provide valuable insights, but calibration and validation using reference equipment are necessary to ensure accuracy and reliability. Architects, engineers, environmentalists, and policymakers are essential in creating green buildings with high IEQ. Prioritizing occupant comfort, health, and productivity ensures a sustainable and healthy future for future generations. However, continual monitoring and research are needed to improve green building practices and overcome construction problems. Intelligent models for continual on-field calibration of low-cost sensors may improve performance. Green buildings benefit occupants and the environment by optimizing energy use and using IAQ monitoring technologies.

This paper analyses the EU’s policies for energy and climate, using Börzel's (2002) theoretical framework on Europeanisation, and examines member states’ Green Deal responses, strategies, and compliance. As expressed in their final NECPs, although member states’ responses vary, most of the critical components were partially addressed, while the others were largely addressed. We observe a considerable variation in Member State’s strategies. Member States classified as foot-dragging beforehand are fence-sitting now, while those previously categorised as fence-sitting are now either foot-dragging or pace-setting. The root cause of these classification changes for the member states within the EU can be traced back to their internal environments in which the involved stakeholders each have a different response pace regarding environment, climate, and energy. We present and analyse our theoretical context, discuss the EU’s energy policies and the NECPs, examine member states’ responses and compliance with this new framework, and propose several challenges.

In this paper, we propose a method to find all efficient solutions of an interval multiple objective linear programming (IMOLP) problem with interval coefficients in the objective functions, the constraint matrix and the right-hand side vector. The set of all efficient solutions of an IMOLP problem can completely supply information to the decision makers in finding their most preferred solutions but, up to now, not all solutions of an IMOLP problem can be found by known methods. A numerical example is given to illustrate the working of the proposed method.

As in every workplace, human sustainability in dental enterprises is connected to air and water quality, eco-friendly and naturally designed working spaces and 4R’s culture. Purpose of this study is to access the preferences and knowledge on circular economy and green building construction in a sample of students and dentists in Greece. Students (N1=93) and dentists (N2= 126) filled in e-questionnaires from April to December 2022. The response rate was 17,88% for N1 and 1.94% for N2. Data revealed that both students and dentists lack knowledge on circular economy (N1=67.7%, N2=68,25%), EU regulations on amalgam disposal (N1=64.5%, N2=58.73%) and plastic recycling (N1=76.34%, N2=76,98) while they do recycle at home (N1= 80.6%, N2=82.54%) and have participated in voluntary environmental actions (N1=58.1%, N2=66.67%). Gender influences the importance of factors related to green dental practices, with women students being more prone to answer positively as important for increase costs for network changes (p=0.02), lack of environmental awareness of manufacturers (p=0.057), and poor wastewater management (p=0.01). Students from urban areas are more prone to answer positively for lack of state financial support (p=0.02), low level of green design in the building (p=0.03), low direct financial benefit of a green dental office (p=0.04), low benefit to the reputation of the green dental office (p=0.02), lack of continuing education training seminars on green dentistry (p=0.05). For dentists, no significant relationships were observed except of a weak positive relationship in increase in costs for changes related to utility networks (p=0.08) while increased wasteful energy (p=0.12) and waste of dental materials (p=0.19) seemed important only for dentists in urban areas. Women dentists were more prone to answer positively on wasting energy (p=0.024) and use of unapproved disinfection products (p=0.036). The findings contribute ideas and solutions on green dental office buildings and sustainable behaviors through educational activities and social perspective of factors such as age, experience in dentistry, gender, and urbanism. It also inspires future multi-disciplinary research on dental quality assurance, psychology of environmentalism, economics, and behavioral science in dentistry.

Environmental and economic needs drive the increased penetration of intermittent renewable energy in electricity grids, enhancing uncertainty in market conditions prediction and network constraints. Thereafter, the importance of energy systems with flexible dispatch is reinforced, ensuring energy storage as an essential asset for these systems to be able to balance production and demand. In order to do so, such systems should participate in whole-sale energy markets, enabling competition among all players, including conventional power plants. Consequently, an effective dispatch schedule considering market and resource uncertainties is crucial. In this context, an innovative dispatch optimization strategy for schedule planning of renewable systems with storage is presented. Based on an optimization algorithm combined with a machine learning approach, the proposed method develops a financial optimum schedule with the incorporation of uncertainty information. Simulations performed with a concentrated solar power plant model following the proposed optimization strategy demonstrate promising financial improvement with a dynamic and intuitive dispatch planning method, emphasizing the importance of uncertainty treatment on the enhanced quality of renewable systems scheduling.

Efficient train driving plays a vital role in reducing the overall energy consumption in the railway sector. An energy minimising control strategy can be computed using the framework given by optimal control theory, in particular the Pontryagin maximum principle. Our optimisation approach is based on an algorithm presented by Khmelnitsky that considers electric trains equipped with regenerative braking. A derivation of Khmelnitsky’s theory from a more general formulation of the maximum principle is given in this article, and a complete list of switching cases between different driving regimes has been included that is essential for practical application. A number of numerical examples are added to visualise the various switching cases. Energy consumption data from real-life operation of passenger trains are compared to the calculated energy minimum. In the presented study, the optimised strategy was able to save 37 percent of the average energy demand of the train in operation.

Cement production contributes to about 7% of CO2 emissions into the atmosphere. Despite our best efforts, we cannot find a competitive substitute that is both reliable and environmentally friendly. The simplest way to solve the problem is to rationalize the resources and try to minimize its use by substitute them with other materials. A concrete that contains a substantial amount of fly ash is already available, but its use is limited by the current shortage in the market and reduced initial resistance. In this context, ternary mixtures have been used to conduct an experiment. A significant volume of cement has been replaced with fly ash and metakaolin, which serves as a correction factor for the performance. In this work the main advantages and disadvantages of the simultaneous use of these two additions, which may reveal to be very interesting with high volumes of substitution will be presented. The performance of this ternary mixture is very promising. Provide a wide range of possibilities for replacing cement, maintaining or even improving its mechanical and durability properties. A solution for an ecological concrete with enhanced performance for general use in construction as an alternative to conventional concrete.

Although many devices have recently been proposed for pressure regulation and energy harvesting in water distribution and transport networks, very few applications are still documented in the scientific literature. A new in-line Banki turbine with positive outflow pressure and a mobile regulating flap, named PRS, was installed and tested in a real water transport network for pressure and discharge regulation. The PRS turbine was directly connected to a 55 kW asynchronous generator with variable rotational velocity, coupled to an inverter. The start-up tests showed how automatic adjustment of the flap position and the impeller velocity variation are able to change the characteristic curve of the PRS according to the flow delivered by the water manager or to the pressure set-point assigned downstream or upstream of the system, still keeping good efficiency values in hydropower production.

Fog computing is a potential solution to overcome the shortcomings of the cloud computing processing of IoT tasks. These drawbacks can be high latency, location awareness and security, and it is attributed to the distance between IoT devices and servers, network congestion and other variables. Although fog computing has evolved as a solution to these challenges, it is known for having limited resources that need to be consciously utilised, or any of its ad-vantages would be lost. Computational offloading and resource management are critical concerns to be considered to get maximum benefit of the available resource at fog computing systems and benefit from its advantages. Computational offloading and resource management are important issues to be considered to get maximum benefit of the available resource at fog computing systems and benefit from its advantages. In this article, in vehicular traffic applications, we introduce a dynamic online offloading scheme that involves the execution of delay-sensitive ac-tivities. This paper proposes an architecture of a fog node that enables a fog node to adjust its offloading threshold dynamically (i.e., the criteria by which a fog node decides whether tasks should be offloaded rather than executed locally) using two algorithms: dynamic task scheduling (DTS) and dynamic energy control (DEC). These algorithms seek to solve an optimisation problem aimed at minimising overall delay, improving throughput, and minimising energy consumption at the fog layer, while maximising the use of resource-constrained fog nodes. Compared with other benchmarks, our approach can reduce the delay by up to 95.38% and reduce energy consumption by up to 67.71% in fog nodes. Additionally, this approach enhances throughput by 71.08%.

Criticism has been directed towards the traditional methods of management accounting employed by organizations thus far, as they fail to adequately address the growing need for competitiveness and sustainable development in a globalized environment. This criticism stems from both economic factors, such as insufficient monitoring of material, energy, and escalating overhead costs, and environmental concerns, where inadequate attention is given to vital information regarding significant environmental aspects, impacts, risks, and damages arising from the organization's activities and processes. Additionally, these methods do not sufficiently support the implementation of a functional environmental management system (EMS). Drawing upon our research in EMS standardization following the international standards ISO 1400X, as well as our knowledge gained during the development of the Slovak Technical Standard (STN) for ISO 14051, this paper presents an innovative model of environmental management accounting. This model is based on the cost accounting of material and energy flows within organizations. It serves as an enhancement framework for organizations with an existing EMS built according to the EN ISO 14001 model, as well as for organizations lacking a formalized EMS based on this standard. Within this paper, we specify, decompose, and apply the new model, focusing on the cost accounting of material-energy flows in a hypothetical organization. We meticulously examine the ten key steps of the model's structure, particularly in terms of costing and the allocation of costs to material, energy, system, and waste management.

Buildings use 30-40 % of all energy resources and are thus their main consumers in modern society. Moreover, buildings require a vast amount of different raw materials. During the last two decades, several green building certifications have been created in order to consider social, economic and environmental aspects of sustainability of buildings. One of the most famous and widely used of these certifications is Leadership in Energy and Environmental Design (LEED). So far, the use of LEED has concentrated in the US and other developed countries. One reason that restricts the use of this point-based system certification in developing countries is the limited data about its costs. In this study, the extra cost of the certification process will be evaluated besides the changes needed in the design of the building to reach the points required by LEED. At the first stage, the number of points the case study earns in its current format (Scenario 1) were found out, then the cost difference of getting either the Certified (Scenario 2) or Silver (Scenario 3) level LEED certification for the building was studied. It was found that besides some technical considerations, filling the criteria of the Certified and Silver level increases the total costs of construction by 3.4% and 5.9%. Further improvement of the building’s energy efficiency would enable the attainment of a higher-level certification. The results of the study could help to promote the use of green building certifications in Western Asia.

The Energy Performance of Buildings Directive 2010 calls for the Nearly Zero Energy Standard for new buildings from 2021 onwards: Buildings using “almost no energy” are powered by renewable sources or energy produced by the building itself. For residential buildings, this ambitious new standard has already been reached. But for other building types this goal is still far away. The potential of these buildings to meet a Nearly Zero Energy Standard was investigated by analyzing ten case studies representing non-residential buildings with different uses. The analysis shows that the primary characteristics common to critical building types are a dense building context with a very high degree of technical installation (such as hospital, research and laboratory buildings). The large primary energy demand of these types of buildings cannot be compensated by building and property-related energy generation including off-site renewables. If the future Nearly Zero Energy Standard were to be defined with lower requirements because of this, the state related properties of Bavaria suggest that the real potential energy savings available in at least 85% of all new buildings would be insufficiently exploited. Therefore, it would be useful to instead individualize the legal energy verification process for new buildings to distinguish critical building types such as laboratories and hospitals.

From the linear Nash-Green fluctuations of background metric, we present the perturbation equations in an embedded four space-time. In the context of a five-dimensional bulk, we show that the perturbations are only propagated by the gravitational tensorial field equation. In a Newtonian conformal gauge, we study the matter density evolution in sub-horizon regime and on how such scale may be affected by the extrinsic curvature. We use the "extended Gold 2018'' growth dataset with 25 datapoints and the best fit Planck2018/LambdaCDM parameters. Hence, we determine the evolution equation for the density growth delta(a) as a result from the embedded equations of the background geometry. By using solar constraints, we analyse the evolution of the effective Newtonian constant G_eff and showing that applying Taylor expansion to G_eff (a) under the constraint of time-derivative of G_eff(0)=G at a= 1 in matter domination era, we get an agreement with Big Bang Nucleosynthesis (BBN) and also an alleviation of the 3-sigma tension to 1-sigma contour between (sigma8-Omega_m) of the observations from Cosmic Microwave Background (CMB) and Large Scale Structure (LSS) probes.

In many ports, the ship's speed is limited for the safety of navigation. At the same time, ship captains and port pilots choose the speed of the ship, but not higher than the permitted speed of the ship in the port, therefore the speed of the ship also depends on the experience of the ship captains and port pilots and the sailing conditions of the ship in specific conditions. Choosing the optimal speed of ships in port, including the effect of shallow depth, can reduce fuel consumption and ship emissions in ports, which is important for the development of green and sustainable ports. In all cases, shipping safety is the highest priority. The main objectives of the article are determining the optimal speed of ships in ports with low clearance, ensuring navigational safety, reducing fuel consumption and emissions, and at the same time creating a sustainable port. The article presents the methodology of optimal ship speed calculation, minimum ship controllable speed maintenance, fuel consumption and emission reduction methodology and their impact on sustainable and green maritime transport and port development. The developed methodology was tested on real ships and with the help of a calibrated simulator, sailing through harbor channels and harbor waters in low clearance conditions.

The increasing expansion of the use of renewable distributed generation (RDG) technologies in electrical networks such as solar plants or wind farms and others has changed the nature of the distributed system and transformed it from the traditional system into an efficient system. This system has led to various technical effects in the host networks, with regard to voltage, electrical energy losses, fault levels, stability and electrical energy quality. These effects are not generic to any network; It can be positive, negative or neutral depending on the structure of the studied network, its operational condition and the various technology of the distributed generators. This research aims to quantitatively correct the effects of (RDG) in terms of voltage and electrical energy losses in the host networks. , since efficient distribution systems contain generators of a special nature and often contain bidirectional flux energy, it was necessary to construct a mathematical model of the distribution system hosting RDG units, which is fundamentally different from the traditional model of defining the reference electrical distribution bus bar. In order to achieve the systematic analysis, evaluation criteria and indicators were determined and mathematical formulas for them were completed. Based on the accomplished mathematical basis, an algorithm designed in such a way that it can be applied to any multi-voltage distribution system and to different distributed regenerative generation techniques is proposed. To demonstrate the algorithm's capabilities, it was tested on typical distribution networks using Factory Power DigSilent software, which is somewhat similar to MATLAB but more suitable for large capacity systems.

In this paper, we propose an adaptive duty-cycled hybrid X-MAC (ADX-MAC) protocol for energy-efficient forest fire prediction. The X-MAC protocol acquires the additional environmental status collected by each forest fire monitoring sensor for a certain period. And, based on these values, the length of sleep interval of duty-cycle is changed to efficiently calculate the risk of occurrence of forest fire according to the mountain environment. The performance of the proposed ADX-MAC protocol was verified through experiments the proposed ADX-MAC protocol improves throughput by 19% and was more energy-efficient by 24% compared to X-MAC protocol. As the probability of forest fires increases, the length of the duty cycle is shortened, confirming that the forest fires are detected at a faster cycle.

Offshore electricity production, mainly by wind turbines, and eventually floating PV in the future, will contribute to increase renewable energy production, as well as their dispatchability. The need for that energy evacuation and the implementation of the hydrogen economy are challenges that arise the evaluation of such energy facilities for the direct production of hydrogen. The analysis of the alternatives for the evacuation of offshore renewable electricity for the production of hydrogen is presented. The analysis includes discussion about the current state of the art of hydrogen pipelines and subsea cables, as well as the storage and bunkering system that is needed on shore. The main options for energy evacuation will be via an electric subsea cable to produce hydrogen onshore, or by pipeline, depending on the capacity of the offshore plant. For low capacity, an electric subsea cable is the best option. For high capacity renewable offshore plants, pipelines start to be competitive for distances above aprox. 450 miles.

This study assessed the acute effects of oral methylliberine (DynamineTM) supplementation on cognitive function and indices of well-being. This was a double-blind, randomized, within-subject crossover trial. 25 healthy men and women (33.5±10.7yr, 172.7±8.6cm, 73.3±11.0kg) underwent pretesting before ingesting methylliberine (100mg) or placebo (PLA) for 3 days. On the fourth day participants were tested before their 4th dose (baseline) and every hour post-ingestion for 3 hours. After a one-week washout period, participants repeated testing with the alternate investigational product. The testing battery consisted of vitals, Stroop test and Trail Making Test-B, and visual analog scales that assessed various indices of well-being. Mixed factorial ANOVA with repeated measures were used to assess all variables. There were significant (p≤0.050) interactions in concentration, motivation, and mood. Methylliberine improved concentration 1- and 3-hours, motivation 3 hours, and mood 1-, 2-, and 3 hours (p≤0.050). Methylliberine improved energy, sustained energy, and mood in all participants to a greater extent than PLA at 1-hour and 3-hours relative to baseline (p≤0.050). PLA improved motivation at 1- and 2-hours and mood at 2-hours (p≤0.050). Methylliberine improved concentration, well-being, and the ability to tolerate stress to a greater extent than PLA at 3-hours relative to baseline (p≤0.050). Women observed elevations in sustained energy 1- and 3-hours (p≤0.050) in methylliberine vs. PLA. Methylliberine had negligible influence on cognitive function, vitals (p>0.050), and no adverse events were reported. Methylliberine significantly improved subjective feelings of energy, concentration, motivation, and mood, but not cognitive function. PLA improved motivation and mood at hours 1 and 2, while methylliberine sustained benefits for longer. Methylliberine also improved concentration, well-being, and the ability to tolerate stress to a greater degree than PLA while having no detrimental effects on vital signs. Methylliberine also seems to have a positive impact on sustained energy in women.

Implementing High-Performance Computing to solve In-Memory Data Grid (IMDG)-based energy infrastructure resilience research problems in a heterogeneous environment presents a challenge to workflow management systems. Large-scale energy infrastructure needs multi-variant planning and tools to allocate and dispatch resources in IMDG taking into account the subject domain specificity, resource characteristics, and constraints and quotas for resource use. To that end, we propose a workflow management system using our Orlando Tools framework. To scale computing resources, we provide their integration and use corresponding software to determine key application parameters that can significantly impact the processed data size and the required number of allocated resources. We automated the IMDG cluster launch for the workflow executions. To demonstrate the advantage of our solution, we apply it to evaluate the resilience of the existing energy infrastructure model. Compared to similar approaches, our approach explores large infrastructures by modeling many simultaneously failed elements of different types up to the number of network elements. In terms of problem-solving efficiency and resource use, we achieve near-linear speedup with increasing the number of nodes of each resource.

One of the key geophysical technologies for the energy industry during energy transition to zero footprint is fluid imaging. Knowledge of fluid distribution allows better, more optimized production reducing thus CO2 footprint per barrel produced and for CO2 storage the knowledge of where stored fluids go is mandatory to monitor reservoir seals. Electromagnetic is the preferred way to image fluid due to its strong coupling to the fluid resistivity. Unfortunately, acquiring and interpreting the data takes too long to contribute significantly to field operation and cost optimization. Using artificial intelligence and Cloud based data acquisition we can reduce the operational feedback to near real time and for the interpretation to close to 24 h. This then opens new door for the usefulness of this technology from exploration, monitoring and allows the application envelope to be enlarged to much noisier environment where real time acquisition can be optimized based on the acquired data.

AbstractTo overcome the negative impacts on the environment and other problems associated with fossil fuels have forced many countries to inquire into and change to environmentally friendly alternatives that are renewable to sustain the increasing energy demand. Solar energy is one of the best renewable energy sources with the least negative impacts on the environment. Different countries have formulated solar energy policies to reduce dependence on fossil fuel and increasing domestic energy production by solar energy. According to the 2010 BP Statistical Energy Survey, the world cumulative installed solar energy capacity was 22928.9 MW in 2009, a change of 46.9% compared to 2008. In this study, a PV generation system has been modeled and installed considering uncertain whether based on the hourly wind speed data of New York City (NYC) of year 2014. Regression models has been used to forecast the hourly, weekly, and monthly wind speed of NYC year 2014. Design of experiment (DOE) has been used to determine the optimal panel size (area), the battery capacity size, and other levels of factors.

In the face of escalating climate concerns and the push for sustainable development, the global shift towards renewable and decentralized energy systems presents new challenges and opportunities. This study investigates integrating decentralized energy production, particularly photovoltaic (PV) systems, into national energy planning, aiming to optimize energy strategies that balance local production and consumption with national objectives. By analyzing the Swiss energy model, the research employs the EnergyScope and REHO models to assess the strategic implications of decentralized versus centralized energy systems. Results show that a decentralized approach can significantly reduce PV installation needs to 35 GW, about 23% of potential capacity, and decrease annual system costs by 10% to CHF 1230 per capita. This strategy emphasizes local consumption, minimizes grid reinforcement demands, and leverages economic advantages while addressing overproduction challenges through effective energy storage and grid management. Conclusions underline the strategic value of combining centralized and decentralized methods for resilient and sustainable energy planning. The study contributes to the discourse on energy policy and infrastructure planning, advocating for a hybrid model that accommodates both local conditions and broader energy objectives, urging further research into climate impacts and technology integration for a comprehensive energy future.

The world is moving to the next phase of the energy transition with high penetrations of renewable energy. Flexible and scalable redox flow battery (RFB) technology is expected to play an important role in ensuring electricity network security and reliability. Continuous performance improvements will further enhance their value by reducing parasitic losses and maximizing available energy conversion over broader operating conditions. Concentration overpotentials from poor internal reactant distribution at high and low states of charge (SOC) limit power densities and are thus an important area of investigation. However, efforts to address these coupled electrochemical phenomena can compromise mechanical performance. Modelling and simulation of cell design innovations have shown it is possible to reduce losses from pump energy while increasing the availability of active species where required. The combination of wedge-shaped cells with static mixers investigated in this paper can reduce pressure drop and improve energy efficiency. Toroidal vanadium redox flow battery (VRB/VRFB) designs incorporating this innovation are presented for further development to improve community engagement with the technology.

The level of development of the innovation factor plays a crucial role in supporting the high-quality development of China's economy. To advance the development of regional innovation factor allocation efficiency along the Belt and Road. This article introduces the super-efficiency SBM-DEA-Malmquist model for the static and dynamic analysis of innovation factor allocation efficiency in 17 provinces along the Belt and Road from 2012 to 2021. The Moran index model analyzes the spatial correlation. The results show that: (1) The overall innovation factor allocation efficiency along the Belt and Road is not high, and there are obvious differences among different regions. The eastern region is the highest compared to other regions. (2) According to the efficiency decomposition results, pure technical efficiency is the main reason for the low innovation factor allocation efficiency. (3) Through the Malmquist index and decomposition index, it is found that pure technical efficiency and scale efficiency are key factors in improving technical efficiency. (4) The analysis of spatial correlation reveals a strong spatial agglomeration feature among the provinces along the Belt and Road. The innovation factor allocation efficiency is mainly manifested in the L-L type. Finally, according to the analysis results, suggestions and policies are put forward to improve the innovation factor allocation efficiency in the regions along the Belt and Road.

This paper introduces an innovative energy circuit that challenges conventional understandings of energy conservation, marking a significant departure from traditional frameworks. Seeking to enhance energy generation within existing paradigms, this innovation holds promise for addressing pressing global issues such as the energy crisis and environmental sustainability, while also stimulating scientific inquiry. In contrast to prevalent misconceptions rooted in philosophical and scientific constraints, this study refutes the notion of generating energy from nothing and questions the viability of perpetual motion machines as conceptual models. Instead, it proposes a novel circuit design derived from an unconventional electrical short circuit, which is shown to disrupt the established principles of energy conservation. This circuit offers a transformative approach with diverse applications, including self-recharging capabilities for electric vehicles, bolstering microgrid infrastructure, and facilitating the integration of renewable energy sources. Transcending conventional scientific boundaries, this paper prompts philosophical reflections on the evolving nature of scientific exploration. Ultimately, the energy circuit represents a pivotal step towards mitigating the global energy crisis by reducing reliance on finite resources and fostering sustainability. The paper concludes by illustrating the circuit's potential to revolutionize energy systems and contribute to a more resilient and sustainable future.

In recent years, unquestionable warnings like the negative impacts of CO2 emissions, the necessity of utilizing sustainable energy sources, and the rising demand for municipal electrification have been issued. In this study, by incorporating two significant assumptions, such as electricity production in close proximity to the business location and only renewable energy resource usage, a modest off-grid hybrid energy system is designed. To construct the system, a number of elements such as wind generators (WG), photovoltaic arrays (PV), battery banks, and bi-directional converters are taken into account. Moreover, a real case in Malmö, Sweden, is considered. To optimize the system, a bi-objective problem is developed, and it is solved by proposing a particle swarm optimization (PSO) approach to provide the load requirements (with a maximum allowance of 0.1% unmet) for a nearby supermarket (approximately 1000 m2). Moreover, to verify the obtained results, the developed system is simulated using HOMER Pro software, and the results are compared and discussed. The contribution of this study is to provide off-grid or local clients around the world with a dependable and affordable option by minimizing both the baseline cost of energy and the net current expenditure in the desired system. The best-obtained results by the proposed PSO offered 160 PVs, 5 WGs, and 350 batteries, respectively, while the best solution found by the simulation method was using 384 PVs, 5 WGs, and 189 batteries for the considered off-grid system.

Due to urban warming, energy demand for cooling buildings is rising. The current study used CitySim to estimate the cooling energy requirements for 40 buildings in Downtown, Dubai using high-resolution climate data from weather research and forecasting (WRF) coupled with single layer urban canopy model (SLUCM). Simulating the four mitigation scenarios allowed for the examination of the reduction in cooling load caused by the addition of greenery at a rate ranging from 25% to 100%. The insulated building's cooling demand reduced by a maximum of 13.89% under 100% GI (M4). Scenario M4 resulted in a reduction of 4.6 kWh/m2 and 3.1 kWh/m2 for the non-insulated and insulated low-rise residential buildings, respectively, while the high-rise buildings saw a reduction of 3.09-4.91 kWh/m2 for the non-insulated and 2.07-3.09 kWh/m2 for insulated buildings. This study offered a potential remedy to deal with the problem of urban heating in subtropical environments.

The most potential renewable energy source for global emissions reductions is green hydrogen production. Iraq is looking into several sources of alternative energy to lessen its dependency on fossil fuels and to considerably cut its carbon dioxide emissions. This research examined the conceptual framework for the production and consumption of green hydrogen in Iraq. On the basis of accessible official and public data from government agencies, the potential capabilities of renewable energy resources are assessed, and certain fair assumptions are also established for a full study and evaluation of a possible hydrogen production in the country. The data presented here demonstrate conclusively that the contributions of renewable energy to the production of green hydrogen are substantial, giving the country prominence in the area of green hydrogen production. A pathway for a green hydrogen economy in the country by 2050 is proposed by this research based on its analysis. It is distributed in three stages: green hydrogen as a fuel for industry; using green hydrogen for fuel cells; and hydrogen commercialization. On the other hand, the research found a number of challenges to the implementation of a green hydrogen production that can be used in Iraq and other developing countries, including technological, economic, and social problems in addition to the related policy consequences.

Although train simulation research is vast, most available network simulators do not track the instantaneous movements and interactions of multiple trains for the computation of energy/fuel consumption. In this paper, we introduce the NeTrainSim simulator for heavy long-haul freight trains on a network of multiple intersecting tracks. Trains are modeled as a series of moving mass points (each car/locomotive is modeled as a point mass) while ensuring safe following distances between them. The simulator considers the motion of the train as a whole and neglects the relative movements between the train cars/locomotives. Furthermore, the powers of the different locomotives are transferred to the first locomotive as such a simplification result in a reduced simulation time without impacting the accuracy of energy consumption estimates. While the different tractive forces are combined, the resistive forces are calculated at their corresponding locations. The output files of the simulator contain pertaining information to the train trajectories and the instantaneous energy consumption levels. A summary file is also provided with the total energy consumed for the full trip and the entire network of trains. Two case studies are conducted to demonstrate the performance of the simulator. The first case study validates the model by comparing the output of NeTrainSim to empirical trajectory data using a basic single-train network. The results confirm that the simulated trajectory is precise enough to estimate the electric energy consumption of the train. The second case study demonstrates the train-following model considering six trains following each other. The results showcase the model’s ability in relation to maintaining safe-following distances between successive trains. Finally, the NeTrainSim is demonstrated to be scalable with computational times of O(n) for less than 50 trains (n) and O(n²) for higher number of trains.

In the field of adaptive building technologies, this research introduces the development of a self-regulating solar shading actuator, that utilizes the thermal shape memory effect. The study places its focus on addressing the actuator’s performance under diverse environmental conditions. Thermal simulations were carried out during the development of the individual components of the actuator and for the prediction of specific switching temperatures. The investigation includes an analysis of the sunshade’s response to varying environmental conditions, emphasizing its effectiveness on clear summer days and identifying challenges during overcast periods. The critical coordination between the solar collector and the shape memory alloy (SMA) wire is examined, shedding light on the impact of SMA temperature dynamics on the actuation performance. Through the integration of simulation data and real-world measurements, the study validates the thermal model for the solar collector, establishing the robustness of the system’s operation. This research work contributes significantly to the development of intelligent actuators and outlines the importance of validation of SMA-based applications under real conditions.

The wide interest in developing green energy technologies stimulates the scientific community to seek, for devices, new substitute material platforms with low environmental impact, ease of production and processing and long-term stability. The synthesis of metal oxide (MO) semiconductors fulfils these requirements and efforts are addressed at optimizing their functional properties, through improvement of charge mobility or energy level alignment. Two MOs have rising perspectives for application in light harvesting devices, mainly for the role of charge selective layers but also as light absorbers, namely MoO3 (an electron blocking layer) and Co3O4 (a small band gap semiconductor). The need to achieve better charge transport has prompted us to attempt doping strategies with vanadium (V) ions that, when combined with oxygen in V2O5, produce a high work function MO. We report on subcritical hydrothermal synthesis of V-doped mesostructures of MoO3 and of Co3O4, in which a tight control of the doping is exerted by tuning the relative amounts of reactants. We accomplished a full analytical characterization of these V-doped MOs that unambiguously demonstrates incorporation of the vanadium ions in the MO crystal lattice, as well as effects on the optical properties and work function. We foresee a promising future use of these materials as charge selective materials in energy devices based on multilayer structures.

Wind turbine upgrades have been spreading in the recent years in the wind energy industry, with the aim of optimizing the efficiency of wind kinetic energy conversion. This kind of interventions has material and labor costs and it is therefore fundamental to estimate realistically the production improvement. Further, the retrofitting of wind turbines sited in harsh environments might exacerbate the stressing conditions to which wind turbines are subjected and consequently might affect the residue lifetime. This work deals with a case of retrofitting: the testing ground is a multi-megawatt wind turbine from a wind farm sited in a very complex terrain. The blades have been optimized by installing vortex generators and passive flow control devices. The complexity of this test case, dictated by the environment and by the features of the data set at disposal, inspires the formulation of a general method for estimating production upgrades, based on multivariate linear modeling of the power output of the upgraded wind turbine. The method is a distinctive part of the outcome of this work because it is generalizable to the study of whatever wind turbine upgrade and it is adaptable to the features of the data sets at disposal. In particular, applying this model to the test case of interest, it arises that the upgrade increases the annual energy production of the wind turbine of an amount of the order of the 2%. This quantity is of the same order of magnitude, albeit non-negligibly lower, than the estimate based on the assumption of ideal wind conditions. Therefore, it arises that complex wind conditions might affect the efficiency of wind turbine upgrades and it is therefore important to estimate their impact using data from wind turbines operating in the real environment of interest.

Global waste generation keeps increasing over the year and requires innovative solutions to minimize their impacts on environmental quality and public health. Predicted 2.2 billion tonnes per year of global municipal waste generation in the year 2025 which 1.6 fold is higher than in 2012. Hence, a strategic plan must be ascertained to overcome the future challenges of MSW locally and globally. Universiti Putra Malaysia (UPM) coined an initiative to demonstrate a showcase pilot plant for green energy production from MSW. Therefore, the data was obtained from the survey and actual sampling within the UPM compound to estimate the MSW generated and it's potentially used for green energy production. It is estimated that 5.0 – 7.0 tonne per day of MSW generated which about 30 - 35% is an organic fraction. Upon separation, the organic fractions were digested into biogas through anaerobic. At the maximum conversion of organic fraction, about 775 kWh of electricity may able to generate from the waste. In this study, the complete biorefinery setup and utilize organic components from the MSW generated in UPM was proposed that the biogas subsequently will be used to produce green energy in the form of electricity or cooking fuel.

The concept of distributed generation has made photovoltaic an integral source of energy in smart grid systems, especially in peer-to-peer energy trading frameworks that exploit excess power to fulfill the energy requirements of consumers in cost-efficient and eco-friendly manner. It is believed that P2P energy trading will dominate a significant portion of research in forthcoming power generation systems due to the excessive rise of energy demands across the globe. Despite a plethora of studies on energy optimization solutions in P2P trading, minimizing nanogrid energy trading cost and efficient energy sharing between consumers and prosumers are deemed among the challenging problems. This study overcomes essential issues overlooked by the contemporary P2P energy trading models by introducing a predictive optimization-oriented nanogrid energy trading model. The proposed study encompasses two stages: (1) predictive optimization model which harnesses BD-LSTM-based forecasted energy parameters (energy load, energy consumption, and PV generation) that are later incorporated in PSO-enabled objective function to reduce nanogrid trading cost, (2) optimal energy sharing plan is devised to decide the role of nanogrids as prosumers or consumers by emphasizing the use of PV-produced energy. The proposed model is validated on the case study containing nanogrid houses data. The simulation provides detailed experiments by comparing the energy demand and response using the proposed energy sharing model. The outcomes yield that the energy sharing plan holds a significant potential to fulfill maximum energy requirements of nanogrid house in P2P cluster and significantly reduces the energy cost compared to grid.

Herein, we present the design technique of a resonant rectifier for piezoelectric (PE) energy harvesting. We propose two diode equivalents to reduce the voltage drop in the rectifier operation, a minuscule-drop-diode equivalent (MDDE) and a low-drop-diode equivalent (LDDE). The diode equivalents are embedded in resonant rectifier integrated circuits (ICs), which use symmetric bias-flip to reduce the power wasted for charging and discharging the internal capacitance of a PE transducer. The self-startup function is supported by synchronously generating control pulses for the bias-flip from the PE transducer. Two resonant rectifier ICs, using both MDDE and LDDE, are fabricated in a 0.18 μm CMOS process and their performances are characterized under external and self-power conditions. Under the external-power condition, the rectifier using LDDE delivers an output power POUT of 564 μW and a rectifier output voltage VRECT of 3.36 V with a power conversion efficiency (PCE) of 90.1%. Under self-power conditions, the rectifier using MDDE delivers a POUT of 288 μW and a VRECT of 2.4 V with a corresponding PCE of 74.6%. The result shows that the power extraction capability of the proposed rectifier is 5.9 and 3.0 times higher than that of a conventional full-bridge rectifier.

AI applications are transforming the technology and digital sector, seamlessly integrating into the daily interactions of individuals with the digital world. Key entry points to the internet, such as search engines, are now complemented by recommendation systems that curate content on social networks, streaming platforms, and online marketplaces. Additionally, virtual assistants have become ubiquitous across various platforms. While these groundbreaking changes are gradually extending to several industries, there remains significant untapped potential for the application of artificial intelligence in diverse domains. The renewable energy sector, in particular, continually reinvents itself through research and innovation to meet the escalating demand. Positioned at the forefront of technological evolution, this industry is well-placed to effectively adopt and tailor the innovations of artificial intelligence to its specific needs. Presently, both emerging and established tools leverage elements of AI to enhance processes and introduce functionalities that were previously unattainable. If the current trajectory persists, AI-enhanced tools have the potential to assume a role as crucial to the future of engineering as computer-aided design (CAD) software is in contemporary times.

Recent years have seen a rapid uptake in distributed energy resources (DER). Such technologies pose a number of challenges to network operators, which can ultimately limit the amount of rooftop solar photovoltaics (PV) systems that can be connected to a network. The objective of this industry-based research was to determine the potential network effects of forecast levels of customer owned rooftop solar PV on Energy Queensland’s distribution network and formulate functions that can be used to determine such effects without the requirement for detailed network modeling and analysis. In this research, many of Energy Queensland’s distribution feeders were modelled using DIgSILENT PowerFactory and analyzed with forecast levels of solar PV and customer load. Python scripts were used to automate this process and quasi dynamic simulation (QDSL) models were used to represent the dynamic volt-watt and volt-var response of inverters, as mandated by the Australian Standard AS/NZS 4777. In analyzing the results, linear regression was used to form trend equations that represent various network characteristics against the number of PV connections. The trend equations provide a way of approximating network characteristics for other feeders under various levels of customer owned rooftop solar PV without the need for detail modeling.

The conserved cyanobacterial protein PipX is part of a complex interaction network with regulators involved in essential processes that include metabolic homeostasis or ribosome assembly. Because PipX interactions depend on the relative levels of their different partners and of the effector molecules binding to them, in vivo studies are required to understand the physiological significance and contribution of environmental factors to regulation of PipX complexes. Here we have used the NanoBiT complementation system to analyse regulation of complex formation in Synechococcus elongatus between PipX and each of its two best characterized partners, PII and NtcA. Our results confirm previous in vitro analysis on regulation of PipX-PII and PipX-NtcA complexes by 2-oxoglutarate and on regulation of PipX-PII by the ATP/ADP ratio, showing the disruption of PipX-NtcA complexes by increased levels of ADP-bound PII in Synechococcus elongatus. The demonstration of a positive role of PII on PipX-NtcA complexes under the initial response to nitrogen starvation or the impact of a PipX point mutation on the activity of PipX-PII and PipX-NtcA reporters are further indications of the sensitivity of the system. This study reveals additional regulatory complexities in the PipX interaction network, opening the way to future research in cyanobacteria.

This paper describes a novel concept of producing energy efficient ”Maroon enriched natural gas “ and then Pink hydrogen” from any hydrocarbon base. The key idea is the extraction of hydrogen from water in addition to that from the hydrocarbon in an optimal fashion. This has the benefit of higher water vapor to CO2 exhaust ratio than conventional carbonaceous fuels when generating energy via combustion, a prudent step in achieving Netzero goals in a shorter time, and creating energy independence in most places.. The process of production makes concentrated CO₂ available for use and or sequestration. The process also maximizes use of renewable electricity in hydrogen generation, and maximizes use of existing infrastructure, with a minimum capital cost by energy recycle in the process. The process design applies sound thermodynamic principles which evolved during the nineteenth century, and mimics the geochemical processes going on in some of the natural 'colorless hydrogen'.

The Mine Internet of Things (MIoT), as a key technology for reconstructing post-disaster communication networks, enables to realize the safety monitoring and controlling of the affected roadway. However, due to the challenging underground mine environment, the MIoT suffers from severe signal attenuation, vulnerable nodes, and limited energy, which result in low network reliability of the post-disaster MIoT. To improve the transmission reliability as well as to reduce energy consumption, a directional-area-forwarding-based energy-efficient opportunistic routing (DEOR) for the post-disaster MIoT is proposed. DEOR defines a forwarding zone (FZ) for each node to route packets toward the sink. The candidate forwarding set (CFS) is constructed by the nodes within the FZ that satisfy the energy constraint and the neighboring node degree constraint. The nodes in CFS are prioritized based on the routing quality evaluation by taking the local attributes of nodes into consideration, such as the directional angle, transmission distance, and residual energy. DEOR adopts a recovery mechanism to address the issue of void nodes. The simulation results validate that the proposed DEOR outperforms ORR, OBRN and ECSOR in terms of energy consumption, average hop count, packet delivery rate, and network lifetime.

Developing new energy is critical to China's green and low-carbon development. And the New Energy Demonstration City Policy (NEDCP) is a vital innovation policy that inspires the development of new energy. Is the NEDCP facilitating green and low-carbon development, if so, how? Employing unbalanced panel data from 2003 to 2017, the impact of NEDCP on green and low-carbon development was studied by using the "difference in difference" (DID) model. We find that this policy can take significantly role. After various robustness tests, our results are still valid. According to the heterogeneity analysis, non-resource and non-old industrial base cities have a greater positive impact from this policy. The mechanism analysis denotes that the positive policy effect works through upgrading the industrial structure and stimulating urban innovation. The substantial empirical evidence presented in this article supports the continued promotion and implementation of new energy demonstration cities.

Hybrid Renewable Energy Systems (HRES) integrate renewable sources, storage, and optionally conventional energies, offering an eco-friendly solution to fossil fuels. Microgrids (MGs) bolster HRES integration, enhancing energy management, resilience, and reliability at various levels. This study, emphasizing the need for refined optimization methods, investigates three themes: renewable energy, microgrid, and multiobjective optimization (MOO), through a bibliometric analysis of 470 Scopus documents from 2010-2023, analyzed with SciMAT software. It segments the research into two periods, 2010-2019 and 2020-2023, revealing a surge in MOO focus, especially in the latter period, with a 35% increase in MOO-related research. This indicates a shift towards com-prehensive energy ecosystem management that balances environmental, technical, and economic elements. The initial focus on MOO, genetic algorithms, and energy management systems has expanded to include smart grids and electric power systems, with MOO remaining a primary theme in the second period. The increased application of Artificial Intelligence (AI) in optimizing HMGS within the MOO framework signals a move towards more sustainable, intelligent energy solutions. Despite progress, challenges remain, including high battery costs, the need for reliable MOO data, the intermittency of renewable energy sources, and HMGS network scalability issues, highlighting directions for future research.

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things computing. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSN. Then, using the CUDA Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes’ diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services.

Rapid increase in urbanization and industrialization due to increase in human population has led to increase in global warming resulting to climate change that affects the ecosystem. Unusual natural disasters such as cyclones, storms, floods, weather fluctuations, rising seal levels, transitional ecosystems and coastal disruptions are major adverse effects of climate change. There are huge worrisome concerns and uncertainties about the future of the planet earth due to climate change caused by high carbon emissions. Global aggressive policies in control of Carbon-dioxide (CO2) emissions from several combustion mechanisms have paved way for alternative/ clean energy sources. Green hydrogen is a promising means of clean energy sources for commercial power generation stations and other crucial systems such as hydrogen fuel cell vehicles (HFCVs) , motors, drives, backup power for buildings, and also serving as a source of electrical energy for remote locations. The goal of this study is to review the current level of Sub-Saharan African nations’ preparedness to adopt green hydrogen as a way to mitigate local energy supply issues and strengthen their individual economies.

The ubiquitous connectivity for the space-air-ground integrated network (SAGIN) of the beyond fifth generation of communication and sixth generation of communication (B5G/6G) is envisaged to meet the needs for the demanded quality of service (QoS), green communication, and "dual carbon" target. However, the offloading and computation of massive latency-sensitive tasks dramatically increases the energy consumption of the network. Furthermore, the traditional power supply technology of the network base stations (BSs) enhances the carbon emission. To address these issues, we first propose a SAGIN architecture with energy harvesting devices, where the BS is powered by both renewable energy (RE) and the conventional grid. The BS explores wireless power transfer (WPT) technology to power the unmanned aerial vehicle (UAV) for stable network operation. RE sharing between neighbouring BSs is designed to fully utilize RE for reduce carbon emission. Secondly, on the basis of task offloading decision, UAV trajectory, and RE sharing ratio, we construct cost functions with joint latency-oriented, energy consumption, and carbon emission. Then, we develop a twin delayed deep deterministic policy gradient (TD3PG) algorithm based on deep reinforcement learning to minimize the cost function. Finally, simulation results demonstrate that the proposed algorithm outperforms the benchmark algorithm in terms of reducing latency, energy saving, and lower carbon emission.

Due to its spatio-temporal variability, the mismatch between the weather and demand patterns challenges the design of highly renewable energy systems. A principal component analysis is applied to a simplified networked European electricity system with a high share of wind and solar power generation. It reveals a small number of important mismatch patterns, which explain most of the system's required backup and transmission infrastructure. Whereas the first principal component is already able to reproduce most of the temporal mismatch variability for a solar dominated system, a few more principal components are needed for a wind dominated system. Due to its monopole structure the first principal component causes most of the system's backup infrastructure. The next few principal components have a dipole structure and dominate the transmission infrastructure of the renewable electricity network.

In this paper, we study the semilinear pseudo-parabolic equations $\displaystyle u_{t} - \triangle_{\mathbb{B}}u - \triangle_{\mathbb{B}}u_{t} = \left|u\right|^{p-1}u$ on a manifold with conical singularity, where $\triangle _{\mathbb{B}}$ is Fuchsian type Laplace operator investigated with totally characteristic degeneracy on the boundary $x_{1} = 0$. Firstly, we discuss the invariant sets and the vacuum isolating behavior of solutions with the help of a family of potential wells. Then, we derive a threshold result of existence and nonexistence of global weak solution: for the low initial energy $J(u_{0})<d$, the solution is global in time with $I(u_{0}) >0$ or $\displaystyle\Vert \nabla_{\mathbb{B}}u_{0}\Vert_{L_{2}^{\frac{n}{2}}(\mathbb{B})} = 0$ and blows up in finite time with $I(u_{0}) < 0$; for the critical initial energy $J(u_{0}) = d$, the solution is global in time with $I(u_{0}) \geq0$ and blows up in finite time with $I(u_{0}) < 0$. The decay estimate of the energy functional for the global solution and the estimates of the lifespan of local solution are also given.

Purpose of publication: - Preparation of bibliometric data exported from the Scilit platform on energy efficiency and conservation for further analysis to identify relevant research topics. - To identify potential issues in the processing of data exported from the Scilit platform. - Providing colleagues with the opportunity to use the prepared data and examples of their analysis for independent research on topical issues of energy efficiency and energy conservation using materials provided by the Scilit platform. Research Materials: Files in CSV and RIS formats exported from Scilit for the query "energy conservation OR efficiency" in Common Fields [Title, Abstract, Keyword], using filters: Content Type → Journal Article; Year→2021-2023; Subject → Industrial Engineering (29.8K), Energy and Fuel Technology (9.8K), Manufacturing Engineering (9.2K). A total of 30K records sorted by their relevance (10K for each year) were exported. Data are current as of 14-03-2024. Methods: Preprocessing of title, annotation, and keyword field texts using lemmatization dictionaries collected on GitHub, removal of keywords taken from GATE and spaCy, and "manual" editing. Using VOSviewer to analyze publication topics by clustering keywords based on their co-occurrence. Using Scimago Graphica to build bubble diagrams.Application of the GSDMM algorithm for clustering bibliometric records by title and annotation texts. Creation of a dictionary for this algorithm using the keyword field.Use of the Carrot2 demo version and the NMF algorithm for a more detailed analysis of the topics of the record clusters obtained from GSDMM. Results: are presented in the form of initial and interim tables and graphs obtained in the course of this study. The full tables are provided as references to the attached materials. Supplementary material for this preprint on figshare: Chigarev, Boris (2024). Supplementary material for preprint "Analyzing the Possibilities of Using the Scilit Platform to Identify Current Energy Efficiency and Conservation Issues". figshare. Dataset. https://doi.org/10.6084/m9.figshare.25574058.v1

In this work, we consider a one-dimensional laminated beam in the case of non-equal wave speeds with only one infinite memory on the effective rotation angle. In this case, we establish the general decay result for the energy of solution without any kind of internal or boundary control. The main result is obtained by applying the method used in Guesmia et al. (Electron. J. Differential Equations 193: 1-45, 2012) and the second-order energy.

As the leader of the largest economy, President of the United States has substantive influence on addressing the global climate change problem. However, presidential election is often dominated by issues other than energy problems. This paper focuses on the on-going 2016 presidential election, examining the energy plans proposed by the leading Democrat and Republican candidates. Our data from the Iowa caucus survey in January 2016 suggests that voters are more concerned about terrorism and economic issues than environmental relative issues. We then compare the Democratic and Republican candidate’s view of American’s energy future, and evaluate their proposed renewable energy targets. We find that the view on renewable energy is polarized between Democratic and Republican candidates, while candidates from both parties agree on the need for energy efficiency. Results from our ordinal least squares regression models suggest that Democratic candidates have moderate to ambitious goals for developing solar and other renewable energy. The Republican candidates favor fossil fuel and they neglect to provide any plan for renewable energy. In addition, this trend of polarization has grown more significant when compared with the past three presidential elections. Our observation suggests that energy issues need to be discussed more to draw broader attention to salient issues of diversifying and decarbonizing the nation’s energy system.

The paper proposes a methodological way to compensate the imbalance between energy generation and consumption using the battery block of electric vehicles as energy reservoir through the well-known Vehicle to Grid system (V2G). The method is based on a simulation process developed by the authors that take into consideration the daily fluctuations of energy consumption as well as the power level generated by an energy source, either conventional, renewable or hybrid. The study shows that for very large electric vehicle fleet the system renders non-viable, since the remaining energy in the battery block to let the electric vehicle usable for running during daytime avoids to compensating for the energy grid imbalance, only allowing to cover a percentage of the energy imbalance, which the proposed methodology may optimize. The analysis of the proposed methodology also shows the viability of the system when being applied to a small fleet of electric vehicles, not only compensating for the energy imbalance but also preserving the required energy in the battery of the electric vehicle to make it run. The method allows predicting the optimum size of electric vehicle battery, which depends on the energy generation level, coverage factor of the energy imbalance and size of the electric vehicle fleet.

Besides the increase in global energy demand, access to clean energy, reduction in greenhouse gas emissions caused by conventional power generation sources, energy security, and availability of electricity in remote villages in emerging nations are some of the factors that foster the use of renewable energy sources (RESs) in generating electricity. One of the aims of initiating microgrids (MGs) is to maximize the benefits of RES and alleviate the associated grid integration issues. Microgrids are made up of RES connected to electrical loads within clearly delineated electrical limits that operate as individual controllable units on the electrical network. It can operate in-dependently as well as in connection with the grid. The paper presents an overview of microgrids and investigates the system's performance when connected to and disconnected from the grid. Furthermore, in both modes of operation, the functioning and behavior of system components such as the bidirectional DC-DC converter and energy storage system (ESS) were evaluated. The architecture of the proposed microgrid consists of a small hydropower plant, a wind farm, and a battery energy storage system (BESS). The microgrid under investigation is modeled and simu-lated using MATLAB and Simulink.

The wave energy converter (WEC) technology constantly develops new designs and models. To encourage its development within the energy sector, research is required to improve current designs and create durable and effective devices. However, it is also crucial to investigate the suitability of a WEC to a site-specific sea condition as this can impact its optimal power absorption and constructability. This study evaluates and compares the efficiency of a heaving buoy with varied geometries in one of the most energetic places along the Mexican coast. A statistical analysis of the wave climate on the coast of Ensenada during the last 42 years is performed to define the conditions to which the device is subjected. The location of the WEC in shallow waters is chosen using a computational model which solves the modified mild slope equation in its elliptic shape. The heaving buoy floater is studied using three distinct geometries: a semi-sphere, a cylinder, and a proposed rounded semi-rectangle. The hydrodynamic response of the three geometries is then analysed in the frequency and time domain using ANSYS AQWA. The hydrodynamic study involves the assessment of the floating body dynamics, exerted forces, the power absorbed as well as the suitability of the proposed power take-off (PTO) system. Findings reveal that the proposed geometry absorbs the most energy, with an annual power of 135.11 MW, and that the transmission PTO design is appropriate for this type of technology.

A major goal of neuroscience is understanding how neurons arrange themselves into neural networks that result in behavior. Most theoretical and experimental efforts have focused on a top-down approach which seeks to identify neuronal correlates of behaviors. This has been accomplished by effectively mapping specific behaviors to distinct neural patterns, or by creating computational models that produce a desired behavioral outcome. Nonetheless, these approaches have only implicitly considered the fact that neural tissue, like any other physical system, is subjected to several restrictions and boundaries of operations.Here, we propose a new, bottom-up conceptual paradigm: The Energy Homeostasis Principle, where the balance between energy income, expenditure, and availability are the key parameters in determining the dynamics of the found neuronal phenomena from molecular to behavioral levels. Neurons display high energy consumption relative to other cells, with metabolic consumption of the brain representing 20% of the whole-body oxygen uptake, contrasting with this organ representing only 2% of the body weight. Also, neurons have specialized surrounding tissue providing the necessary energy which, in the case of the brain, is provided by astrocytes. Moreover, and unlike other cell types with high energy demands such as muscle cells, neurons have strict aerobic metabolism. These facts indicate that neurons are highly sensitive to energy limitations, with Gibb’s free energy dictating the direction of all cellular metabolic processes. From this activity, the largest energy, by far, is expended by action potentials and post-synaptic potentials; therefore, plasticity can be reinterpreted in terms of their energy context. Consequently, neurons, through their synapses, impose energy demands over post-synaptic neurons in a close loop-manner, modulating the dynamics of local circuits. Subsequently, the energy dynamics end up impacting the homeostatic mechanisms of neuronal networks. Furthermore, local energy management also emerges as a neural population property, where most of the energy expenses are triggered by sensory or other modulatory inputs. Local energy management in neurons may be sufficient to explain the emergence of behavior, enabling the assessment of which properties arise in neural circuits and how. Essentially, the proposal of the Energy Homeostasis Principle is also readily testable for simple neuronal networks.