As on 31.03.2020, 55.4 % (205135 MW) of total installed capacity (370106 MW) in India is through coal and lignite based power plants. These plants, set up by central, state and private utilities with substantial capital investment are facing consistently reducing Plant Utilization Factor (known as Plant Load Factor, PLF, in India). In the year 2019-20 the national average thermal power PLF stood at 55.4%, down from 78.6 % in 2007-08. On the other hand, the electricity demand is consistently rising in the country and there exists a peak and energy shortage at national level. In 2019-20 energy shortage was 0.7 % and peak shortage was 0.5 %. A disturbing paradox therefore exists here. On one hand, the country is power deficit, and on the other hand, a large amount of coal based affordable power, ready to be generated by thermal power generators, remains grossly unused. Looking into the fact that considerable investment has gone into developing these thermal power generation assets in the country, the falling PLF is a matter of concern for all the key stakeholders including the power producers, lenders, regulators and consumers. This paper identifies seven major factors that are affecting PLF of thermal power plants and then makes an attempt to project future scenario of PLF so that critical stakeholders can intervene through appropriate actions. Primary research with responses from power professionals has been used to find out the major factors. Future projection of PLF has been done using Partial Least Square (PLS) regression. Projection shows that in the Business As Usual case (Factors increasing at the current CAGR rate), the thermal power plants will face very low level of PLF (14.76 %) by 2024-25. This will mean that many plants will be shut down and many will run for only few hours in a day that too at very low loads. If the future generation mix is kept as indicated by Central Electricity Authority (CEA), a Govt. of India in its report (Draft report on optimal generation capacity mix for 2029-30- CEA- Govt of India) then the thermal power plant average PLF can sustain above 68 % until 2024-25. If followed, this path can be a breather for the thermal power plants.

The article studies and analyzes a hybrid power source system, lithium battery, and fuel cell assisted by flexible photovoltaic panels for hybrid electric vehicles (HEVs). The new configuration operates based on optimizing the performance of the dual power source to maximize the available energy and operating time. The operational mode simulates specific driving conditions in urban, peripheral, and intercity routes under variable driving patterns and traffic conditions. The simulation results show that the new configuration increases the hybrid electric vehicle performance, providing higher reliability despite the more complex design. The proposed system reduces the external power supply dependence and avoids unexpected sudden stops due to energy exhaustion. The new configuration also extends the vehicle driving range depending on driving mode and route conditions. The vehicle driving range extension may reach up to 276.3 km. The hybridization of fuel cells with lithium battery and PV panel assistance enhances battery management, reducing battery servicing and increasing lifespan. The proposed topology is cheaper than a single fuel cell power system by 17.2%, reducing the total cost related to the single battery power system if we consider the battery recharge for its entire lifespan. The cost saving is 2.6% for the American market and 14.8% for the European one.

The epistemology of the traditional risk assessment examined has a taxonomy based upon deterministic, behaviouristic and compatibilist methodologies which are likely driven by neo-liberal market related requirements for bigger, faster cheaper wind-turbines. Analysis of available risk assessments suggested that there is no conscious effort to deceive the reader although the intended audience is unclear. The language, and scoring mechanisms utilized indicates the presence of conformity bias, confirmation bias, and numerical inconsistency leading to data ossification. By reverse engineering risk assessment content, Pragmatic and Social Psychological aspects can be investigated, and the adequacy of the document can be evaluated using evidence-based models.

Electrical microgrids are deemed to be the future of modern power systems. Microgrids are sophisticated, decentralized, and self-sufficient small-scale power systems consisting of various resources ranging from wind turbines, solar photovoltaics, electric vehicles, smart energy storage, and complex communication infrastructure. However, renewable energy sources such as solar photovoltaics and wind-based generators are highly intermittent, and if not appropriately planned, they can compromise the stability of the grid. Formal methods can define and analyze the functionality and behavior of any system and show if the system design is correct before the actual system is implemented. Although formal methods have been around for many years, it is surprising that little to none are utilized in the design of safety-critical electrical power systems. Currently, in modeling microgrids, few to no attempts of formalization are being used to improve the design reliability and reduce system operating costs and time. This work demonstrates how complex systems such as microgrids can be modeled elegantly using a formal specification method. In this work, the Z state-based formal specification language (Z-Method) is used to model and verify microgrid designs. In this work, 3-interconnected microgrid systems with a high penetration level of solar and wind-based renewable energy sources with plugin hybrid electric vehicles (PHEV) as battery energy storage systems (BESS) are modeled using the Z-method. To the best of authors, the knowledge presented formal method is one of the first reported attempts in modeling microgrid communities using Software Engineering formalism.

Synthetic fuels play an important role in the defossilization of future aviation transport. To reduce the ecological impact of remote airports due to long range transportation of kerosene, a decentralized on-site-production of synthetic paraffinic kerosene is applicable, preferably as near-drop-in fuel or alternatively as blend. One possible solution for such a production of synthetic kerosene is the Power-to-Liquid process. The basic development of a simplified plant layout addressing the specific challenges of a decentralized kerosene production which differ from most current approaches for infrastructural well-connected regions is described. The decisive influence of the Fischer-Tropsch synthesis on the PtL process is shown by means of a steady-state reactor model which was developed in Python and serves as basis for further development of a modular environment able to represent entire process chains. The reactor model is based on reaction kinetics according current literature. The effects of adjustments of the main operation parameters on the reactor behavior are evaluated and the impacts on up- and downstream processes are described. The results prove the governing influence of the Fischer-Tropsch reactor on the PtL process and show its flexibility regarding the desired product fraction output, which makes it an appropriate solution for a decentralized kerosene production.

The energy transition towards renewable and more decentral power production triggers the need for grid and storage expansion on all voltage levels. Today's power system planning focuses on certain voltage levels or spatial resolutions. In this work we present an open source software tool eGo which is able to optimize grid and storage expansion throughout all voltage levels in a developed top-down approach. System costs are minimized by applying a linear optimal power flow considering the grid infrastructure of the extra-high and high-voltage (380 to 110 kV) level. Hence, the common differentiation of transmission and distribution grid is partly dissolved, integrating the high-voltage level into the optimization problem. Consecutively, optimized curtailment and storage units are allocated in the medium voltage grid in order to lower medium and low voltage grid expansion needs, that are consequently determined. Here, heuristic optimization methods using the non-linear power flow were developed. Applying the tool on future scenarios we derived cost-efficient grid and storage expansion for all voltage levels in Germany. Due to the integrated approach storage expansion and curtailment can significantly lower grid expansion costs in medium and low voltage grids and at the same time serve the optimal functioning of the overall system. Nevertheless, the cost-reducing effect for the whole of Germany was marginal. Instead, the consideration of realistic, spatially differentiated time series lead to substantial overall savings.

The design and integration of intelligent energy management systems in hybrid Electric Vehicle (EV) charging stations, leveraging Industry 4.0 and renewable energy sources, is crucial for advancing sustainability, efficiency, and technological development. This paper introduces a pioneering hybrid EV charging station that employs hybrid power sources, combining photovoltaic panels, a battery, and a fuel cell, each operating at 240V and 14A. Utilizing wireless power transfer technology, the system comprises five integral blocks: a power source, a boost converter, a High-Frequency (HF) inverter, transfer coils, and EV batteries. The optimization process involves two key stages: i) maximum power point tracking optimization of the boost converter, ensuring maximum power generation by varying the duty cycle between 10% and 90%, and ii) the HF phase employing a class-ϕ2 inverter at 30 MHz, synchronized with the resonant frequency of wireless power transfer coils. Control is executed through a digital signal processor card utilizing zero-voltage switching for efficient operations. This design enhances sustainability in EV charging solutions by optimizing power transfer through the use of hybrid sources.

The global steel industry is a major contributor to greenhouse gas emissions, and the adoption of green hydrogen in Direct Reduced Iron (DRI) production has the potential to significantly reduce CO2 emissions and support climate targets. This review explores the environmental, economic, technological, and social implications of using green hydrogen in DRI processes. The findings suggest that green hydrogen can reduce emissions by up to 90% compared to traditional methods, enhance energy security, and stimulate local economic development. However, high initial costs, energy efficiency concerns, and social acceptance issues remain significant barriers. The review also highlights the importance of integrating carbon capture and storage technologies, addressing logistical challenges, and considering the geopolitical implications of a shift towards green hydrogen. Green hydrogen presents a promising solution for decarbonising the steel industry but continued research, investment in infrastructure, and strategic planning are essential to overcome the challenges and drive widespread adoption.

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 urgency for a shift towards cleaner and more efficient energy alternatives for power generation is underscored by the escalating demand for energy and the detrimental environmental impacts of conventional fossil fuels. In the case of automotive power, electric vehicles are being adopted in many applications, however, challenges remain such as extended charging periods and limited ranges between charges. Therefore, this research study conducts a techno-economic analysis of renewable energy technologies (including solar modules, wind turbines and piezoelectric materials) to augment the efficiency and sustainability of electric buses. The study examines the scope for these technologies to be adopted on double-decker electric buses in London in the United Kingdom. The solar modules are placed on the rooftop and sides of the bus generating 15.9 kWh/day and the wind turbine in the front bumper of the bus generates 8.3 kWh/day. However, the piezoelectric material generated only 22.6 Wh/day, rendering it an impractical inclusion in further analysis. Therefore, both the solar modules and wind turbines combined generate 24.2 kWh/day, which can increase the driving range by 16.3 km per day and this results in savings of 19.36 minutes for charging at the stations. Investing in such projects would have a positive return as the internal rate of return (IRR) and net present value (NPV) are 2.8% and £11,175 respectively. The annual revenue would be £6,712, and the greenhouse gas (GHG) reduction would be two metric tons annually. Electricity exported to the grid, the electricity export rate, and the initial investment are identified as key factors influencing power outage in a sensitivity analysis. In conclusion, this numerical modelling study paves the way for experimental validation towards implementation of renewable energy technologies on electric bus fleets.

Various renewable energy sources such as wind power and photovoltaic (PV) have been increasingly integrated into the power system through power electronic converters in recent years. However, power electronic converter-driven stability issues under specific circumstances, for instance, modal resonances might deteriorate the dynamic performance of the power systems or even threaten the overall stability. In this paper, the integration impact of a hybrid renewable energy source (HRES) system on modal interaction and converter-driven stability is investigated in an IEEE 16-machine 68-bus power system. Firstly, an HRES system is introduced, which consists of full converter-based wind power generation (FCWG) and full converter-based photovoltaic generation (FCPV). The equivalent dynamic models of FCWG and FCPV are then established, followed by the linearized state-space modeling. On this basis, converter-driven stability analyses are performed to reveal the modal resonance mechanisms of the interconnected power systems and the modal interaction phenomenon. Additionally, time-domain simulations are conducted to verify effectiveness of dynamic models and support the converter-driven stability analysis results. To avoid detrimental modal resonances, an optimization strategy is further proposed by retuning the controller parameters of the HRES system. The overall results demonstrate the modal interaction effect between external AC power system and the HRES system and its various impacts on converter-driven stability.

Societies around the world face serious energy problems related to the consumption of fossil fuels and the emission of dangerous radiation. To solve these problems, a new superconductor exhibiting a critical temperature higher than room temperature has been pursued but not achieved. This paper proposes a new energy generation system based on a circuit approach. Secondary to this process, a new type of superconductivity without refrigeration is demonstrated. In our previous paper [1], this system was proposed, but it did not describe the underlying theory in detail and did not mention an actual method to generate energy from the system. The present paper describes the theory of the existence of divergent current density and new superconductivity with no refrigeration. Moreover, the present paper proposes a method for extracting energy from the system by employing a voltage-controlled current source (i.e., a voltage–current converting method).The principle of the system is based on a circuit of two loops and independent current sources. First, the two electric loops are prepared, each with 4 diodes, where the diodes are oriented in the same direction within each loop, but their direction is opposite from loop to loop; four independent current sources connect the loops. In this circuit system, current is added iteratively as it flows along the loop according to Kirchhoff’s circuit law. As a result, a large current and electric potential are present along the loop. To confirm that this system works properly, it is necessary to demonstrate the presence of divergent currents in the transient state, and to do this, the present paper employs the Dirac equation and Lorentz conservation. Electric circuit software is employed to demonstrate that the presented method generates energy actually from our system.Our results confirm the presence of divergent current at a connected point of an independent current source in the transient state. Moreover, in the steady state, the theory demonstrates the Meissner effect (i.e., a London equation) and a new type of macroscopic wave function and condensation. For an initial small input current of 0.1 μA, the simulation reveals a large generating current of 7 kA and electric power of 10¹¹ W, which is much larger than unit of power from an average thermal power station; moreover, the system presents with superconducting electrical transport conditions.The present study is significant because it demonstrates theoretically the existence of divergent current density and a new type of superconductivity requiring no refrigeration. Secondly, the simulations show the generation of a large energy density that can be obtained in a small laboratory room with minimal cost.

Electrical energy demand increase does evolve rapidly due to several socioeconomic factors such as industrialization, population growth, urbanization and of course the evolution of modern technologies in this 4th industrial revolution era. Such rapid increase in energy demand introduces a huge challenge in power system. Such has paved way for network operators to seek for alternative energy resources other than the conventional fossil fuel system. Hence, the penetration of renewable energy into the electricity supply mix has evolved rapidly in the past three decades. However, the grid system has to be well planned ahead to accommodate such increase in energy demand in the long run. Transmission Network Expansion Planning (TNEP) is a well ordered and profitable expansion of power facilities that meets the expected electric energy demand with an allowable degree of reliability. This paper proposes a TNEP model that minimises the network reinforcements, operational costs and costs of renewable energy penetrations, while satisfying the increase in demand. The problem is formulated as a mixed integer linear programming (MILP) problem. The developed model has been tested in several IEEE test systems in multi-period scenarios. The paper also carried out a detailed derivation of the new non-negative variables in terms of the power flow magnitudes, the bus voltage phase angles and the lines’ phase angles for proper mixed integer variables’ decomposition techniques. Moreover, this paper tends to provide additional recommendation in terms of which particular year (within 20 years of planning period) can the network operators install new line(s), new corridor(s) and/or additional generation capacity to the respective existing power networks. Such is achieved by running incremental periods simulations from base year through the planning horizon. The results show the efficacy of the developed model in solving the TNEP problem with a reduced and acceptable computation time even for large power grid system.

Solar energy is a renewable resource of energy that is broadly utilized and has the least emissions among the renewable energies. In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the inputs variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced approaches and evaluate their performance. The proposed LSSVM model outperformed ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.

This study investigates how a range of economic, environmental, and social indicators have influenced the concept of inclusive growth in 19 member countries of the Asian Cooperation Dialogue over the period spanning from 1995 to 2021. To analyze these relationships, the research employs the Driscoll Kraay's Standard Errors regression technique, designed to account for factors such as cross-sectional dependence, hetero-scedasticity, and autocorrelation within the data. Furthermore, various preliminary tests were conducted to assess the data for cross-sectional dependence, slope heterogeneity, heteroscedasticity, and autocorrelation. Additionally, the Westerlund cointegration test was employed to evaluate the potential long-term equilibrium relationships among the variables. The findings reveal that the influence of independent variables on the dependent variable, which in this case is the level of inclusive growth (ING), differs significantly among three distinct income categories: lower-middle-income countries (LMYCs), upper-middle-income countries (UMYCs), and high-income countries (HYCs). Notably, the results underscore the overall statistical significance and robust fit of the regression model for all three income groups. One of the primary contribu-tions of this research is the provision of empirical evidence concerning the role played by fishery and aquaculture production in fostering inclu-sive growth in the Asian context. This research also highlights the trade-offs between economic development and environmental sustainability in terms of trade openness, agriculture, forestry and fishing, ecological footprint, and renewable energy utilization. Enhancing inclusive growth in Asia requires improving fishery and aquaculture management, diversifying economic activities, reducing ecological footprint, and increasing renewable energy utilization. The paper suggests some future work directions for extending the analysis to other regions and indicators, as well as incorporating dynamic panel data models and causality tests. The paper also suggests some policy implications for fostering inclusive growth in Asia through regional cooperation, capacity building, technology transfer and green financing.

This article presents a comprehensive study on enhancing grid resilience through advanced forecasting and optimization techniques in the context of power outages. Power outages pose significant challenges to modern societies, affecting various sectors such as industries, households, and critical infrastructures. The research combines statistical analysis, machine learning algorithms, and optimization methods to address this issue to develop a holistic approach for predicting and mitigating power outage events. The proposed methodology involves the use of Monte Carlo simulations in MATLAB for future outage prediction, Long Short-Term Memory (LSTM) networks for forecasting solar irradiance and load profiles, and a hybrid LSTM-Particle Swarm Optimization (PSO) model to improve accuracy. Furthermore, the role of Battery State of Charge (SoC) in enhancing system resilience is explored. The study also assesses the techno-economic advantages of a grid-tied microgrid integrated with solar panels and batteries over conventional grid systems. The results highlight the potential of the proposed approach in strengthening grid resilience, reducing downtime, and fostering sustainable energy utilization.

As renewable energy generation prediction system has been introduced into the energy trading market, making a model to accurately predict the quantity of solar photovoltaic (PV) energy generation has become a significant problem. Moreover, to encourage an accurate prediction of the quantity of energy generation, an incentive system has been implemented for those who predict the quantity of solar PV energy under the error rate of 8%. Therefore, it has become more important to investigate and analyze current prediction technology numerically and develop more advanced prediction system. In this study, we tried to develop a better model to improve the accuracy of solar PV energy generation quantity by comparing three models made with gradient boosting machine (GBM), Model 1, Model 2, Model 3 respectively. Model 1 was built with the whole training data set without any additional preprocessing. After conducting some additional preprocessing procedure to predict solar energy generation more accurately, we made Model 2 with the whole training data set and Model 3 with only upper 10% of energy generation capacity. To compare the accuracy of three models, normalized mean absolute error (nMAE) was used as an evaluation index. The nMAE of Model 1 was 9.64% while the Model 2 showed 8.41%. Also, Model 3, which was constructed with the training set of upper 10% energy generation capacity, outperformed with the nMAE of 8.08%. For further study, to check the effectiveness of models constructed with GBM, a time series model, autoregressive integrated moving average (ARIMA), was also built and the nMAE was compared.

The thermoelectric generator (TEG) has much potential as a renewable energy technology that could be used in ocean thermal energy conversion (OTEC). This technique employs TEGs and heat exchangers as substitutes for the intricate elements found in conventional OTEC systems, including pressure valves, evaporators, condensers, and turbine generators. By harnessing the temperature difference between warm surface seawater and cold deep seawater, it facilitates the conversion of this thermal gradient into electrical energy. This study used computer simulations to look at and compare three different types of heat exchangers: a cavity-type heat exchanger, a heat exchanger with flat fins, and a heat exchanger with longitudinal vortex generators (LVGs). The objective was to evaluate the influence of these designs on the thermoelectric conversion performance when implemented in OTEC systems. The study's findings indicate that using heat exchanger configurations featuring flat fins, or LVGs, enhances the efficiency of thermal energy transfer from warm seawater to TEGs, resulting in a notable enhancement in the output power of TEGs. Furthermore, the efficiency of TEG thermoelectric conversion is influenced by varying seawater flow rates in relation to the performance of flat fins and LVGs. In contrast to the cavity-type heat exchanger, the utilization of a heat exchanger equipped with flat fins has been found to yield a notable enhancement in the output power of TEGs by approximately 22.92%. Furthermore, this configuration demonstrates a substantial improvement in thermoelectric conversion efficiency, exhibiting an increase of around 40.01%. These improvements are observed under certain conditions pertaining to flow rates and fin heights. Utilizing heat exchangers equipped with LVGs has been found to enhance the output power of TEGs by 13.02% and improve the overall thermoelectric conversion efficiency by 17.72%. These improvements are observed under certain conditions, including specific flow rates, LVG tilt angles, and locations.

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policy-makers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing upon established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale but also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

A conventional electrical grid mostly depends on the electrical power generated from fossil fuels. However, the pollutants from fossil fuels are the key factors for adverse climate change. Most of the developed countries of the world have already recognized the fact that the energy mix requires to be diversified by incorporating renewable energy. This is especially relevant for many of the member countries of the Organization of Islamic Cooperation (OIC), consisting of 57 countries, whose abundance of fossil fuel reserve indicates that much of their electric power is still generated from fossil fuels. In order to integrate renewable energy sources into the hybrid energy mix, an existing conventional grid needs to undergo drastic changes. Alongside this, the population boom in the OIC member countries has caused higher demand for a steady supply of electricity that the conventional grids have long been struggling to cope with. With a view to solving this multifaceted problem, incorporation of the smart power management schemes is indispensable using a smart electrical grid, where information and communications technology is integrated into its major building blocks. This allows advanced applications of a grid, such as the formation of micro-grids, demand-side management, energy storage, high-tech power electronic converters, etc. As the smart grids are being adopted by many developed countries, it is high time for the OIC member countries to pay due attention to this development, if they have not already done so. This paper explains, with special focus on the OIC member countries, the various smart power management technologies, their operations and applications, and the benefits and challenges. Then it goes on to carry out the Strength-Weakness-Opportunity-Threat with Analytical-Heuristic-Procedure (SWOT-AHP) analysis to evaluate its feasibility of incorporation and the underlying strategies appropriate for its implementation. Furthermore, a Hybrid Multi-Criteria-Decision-Making (MCDM) analysis is performed to evaluate the sequence of the emphasis that should be given on each of the technologies from those available for the smart power management initiative. Finally, the study reinforces the stance by drawing parallels from the UN Sustainable Development Goals (SDG) and highlights the importance of the smart grid in line with the global vision of SDG. This paper aims at assisting the decision-makers in implementing smart power management schemes in the OIC member countries, in particular, and other countries of the world, in general.

Solar energy is a renewable resources of energy which is broadly utilized and have the least pollution impact between the available alternatives of fossil fuels. In this investigation, machine leaening approaches of neural networks (NN), neuro-fuzzy and least squares support vector machine (LSSVM) are used to build the models for prediction of the thermal performance of a photovoltaic-thermal solar collector (PV/T) by estimating its efficiency as an output of the model while inlet temperature, flow rate, heat, solar radiation, and heat of sun are input of the designed model. Experimental measurements was prepared by designing a solar collector system and 100 data extracted. Different analyses are also performed to examine the credibility of the introduced approaches revealing great performance. The suggested LSSVM model represented the best performance regarding the mean squared error (MSE) of 0.003 and correlation coefficient (R²) value of 0.99, respectively.

Background: This study examines the relationship between energy usage, health issues, and pro-environmental behavior (PEB) in Kyrgyzstan, amidst the country's commitment to transition from coal-based energy to renewable sources in line with the Paris Agreement. The purpose is to investigate citizens' attitudes towards PEB and their intentions to engage in environmentally friendly actions, focusing on gas, electricity, and coal. Methods: Drawing upon the Theory of Planned Behavior (TPB) framework, a survey was conducted among 1,455 respondents to explore attitudes towards PEB and energy sources' impact on health issues. Results: Decarbonization efforts in Kyrgyzstan and Central Asia are in their early stages, with coal remaining a primary energy source. The study emphasizes the importance of governmental policies and citizen action in achieving decarbonization goals. Rising electricity costs outweigh the increase in indirect energy costs for food, posing challenges for households in adapting to changing energy dynamics. Conclusions: Targeted interventions and communication strategies are crucial to promote pro-environmental behavior and facilitate the transition to sustainable energy sources. Understanding the relationships between health concerns, air pollution awareness, PEB, and energy source choices can inform policymakers and organizations in their efforts to ensure a sustainable and healthy future for Kyrgyzstan and other Central Asian countries.

This study advances the field of solar irradiance nowcasting by introducing a discrete-level classification approach, diverging from traditional continuous measurement methods. Grounded in a need for cost-effective and modular solutions, the research employs high-resolution computer vision coupled with a deep learning framework to predict Direct Normal Irradiance (DNI). By harnessing the capabilities of a Logitech C900 1080p HD camera and an NVIDIA Jetson module, the study achieves real-time data processing, pivotal for CSP systems' operational efficiency. The core of the methodology is the ResNet-50 convolutional neural network, refined via transfer learning on a bespoke dataset, culminating in a predictive accuracy of 85.78%. This discrete classification model contrasts with conventional, costly instruments like the MS-57, offering a novel and accessible alternative for DNI estimation. Such innovation not only demonstrates high predictive accuracy but also signifies a shift towards less resource-intensive and more adaptable solar energy forecasting tools, contributing a significant leap towards optimizing renewable energy systems.

Electricity demand prediction is vital for energy production management and proper exploitation of the present resources. Recently, several novel machine learning (ML) models have been employed for electricity demand prediction to estimate the future prospects of the energy requirements. The main objective of this study is to review the various ML models applied for electricity demand prediction. Through a novel search and taxonomy, the most relevant original research articles in the field are identified and further classified according to the ML modeling technique, perdition type, and the application area. A comprehensive review of the literature identifies the major ML models, their applications and a discussion on the evaluation of their performance. This paper further makes a discussion on the trend and the performance of the ML models. As the result, this research reports an outstanding rise in the accuracy, robustness, precision and the generalization ability of the prediction models using the hybrid and ensemble ML algorithms.