REVIEW | doi:10.20944/preprints201710.0198.v1
Subject: Keywords: Sustainability; energy sources; renewable sources; energy efficiency; energy demand
Online: 31 October 2017 (16:12:05 CET)
Sustainability of current energy policies and known mid-term policies are analised in their multiple facets. First an overview is given about the trend of global energy demand and energy production, analysing the share of energy sources and the geographic distribution of demand, on the basis of statistics and projections published by major agencies. The issue of sustainability of the energy cycle is finally addressed, with specific reference to systems with high share of renewable energy and storage capability, highlighting some promising energy sources and storage approaches.
ARTICLE | doi:10.20944/preprints201908.0180.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: machine learning; smart cities; IoT; deep learning; big data; soft computing; sustainable urban development; building energy; energy demand and consumption; sustainable cities
Online: 17 August 2019 (04:11:44 CEST)
Building energy consumption plays an essential role in urban sustainability. The prediction of the energy demand is also of particular importance for developing smart cities and urban planning. Machine learning has recently contributed to the advancement of methods and technologies to predict demand and consumption for building energy systems. This paper presents a state of the art of machine learning models and evaluates the performance of these models. Through a systematic review and a comprehensive taxonomy, the advances of machine learning are carefully investigated and promising models are introduced.
CASE REPORT | doi:10.20944/preprints201807.0358.v1
Subject: Engineering, General Engineering Keywords: energy diagnosis; energy efficiency; UNAM; IER; energy consumption and demand
Online: 19 July 2018 (11:36:57 CEST)
An energy diagnosis is a tool used to seek the improvement of energy saving measures, environmental conservation and energy efficiency, making relevant its implementation in any kind of buildings. For this article, an energy diagnosis of third level was carried out in buildings of the Instituto de Energías Renovables (IER) from Universidad Nacional Autónoma de México (UNAM) through survey and census of the 36 buildings in the IER, in order to characterize current patterns of energy consumption and demand, and generating specific strategies towards savings and energy efficiency, such as indicators and corrective proposals within and non-financial investment.
ARTICLE | doi:10.20944/preprints202011.0348.v1
Subject: Engineering, Automotive Engineering Keywords: electricity system; COVID-19; electricity demand; energy; demand; behaviour; lockdown; electricity pricing
Online: 12 November 2020 (12:33:48 CET)
The outbreak of SARS-COV-2 disease 2019 (COVID-19) abruptly changed the patterns in electricity consumption, challenging the system operations of forecasting and balancing supply and demand. This is due to the mitigation measures that include lockdown and Work from Home (WFH), which decreased the aggregated demand and remarkably altered its profile. Here, we characterise these changes with various quantitative markers and compare it with pre-COVID-19 business-as-usual data using Great Britain (GB) as a case study. The ripple effects on the generation portfolio, system frequency, forecasting accuracy and imbalance pricing are also analysed. An energy data extraction and pre-processing pipeline that can be used in a variety of similar studies is also presented. Analysis of the GB demand data during the March 2020 lockdown indicates that a shift to WFH will result to a net benefit for flexible stakeholders, such as consumer on variable tariffs. Furthermore, the analysis illustrates a need for faster and more frequent balancing actions, as a result of the increased share of renewable energy in the generation mix. This new equilibrium of energy demand and supply will require a redesign of the existing balancing mechanisms as well as the longer-term power system planning strategies.
ARTICLE | doi:10.20944/preprints202211.0006.v1
Subject: Earth Sciences, Environmental Sciences Keywords: Neural network; Biochemical Oxygen demand; Biosensor; Microbial Fuel Cell
Online: 1 November 2022 (01:22:10 CET)
Biochemical oxygen demand (BOD) is one of the most important factors to consider when evaluating water contamination. BOD5 is the amount of oxygen consumed in five days by microorganisms that oxidize biodegradable organic materials in an aerobic biochemical manner. The primary objective of this effort is to use microbial fuel cells (MFCs) to shorten the time required for BOD5 measurements. We created a regression artificial neural network (AI), and the predictions we obtained for BOD5 measurements were taken over 6 – 24 hours with an average error of just 7%. The outcomes demonstrated by our AI MFC/BES BOD5 sensor’s viability for use in real-world scenarios.
ARTICLE | doi:10.20944/preprints202003.0096.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Deep learning; Energy demand; Temporal convolutional network; Time series forecasting
Online: 5 March 2020 (15:02:37 CET)
Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these type of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand, and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.
ARTICLE | doi:10.20944/preprints202003.0158.v1
Subject: Engineering, Energy & Fuel Technology Keywords: energy; demand; forecasting; deep; learning; machine; convolutional; artificial; neural; networks
Online: 10 March 2020 (03:40:31 CET)
This paper investigates the use of deep learning techniques to perform energy demand forecasting. Specifically, the authors have adapted a deep neural network originally thought for image classification and composed of a convolutional neural network (CNN) followed by a multilayered fully connected artificial neural network (ANN). The convolutional part of the network was fed with a grid of temperature forecasting data distributed in the area of interest in order to extract a featured temperature. The subsequent ANN is then fed with this calculated temperature along with other data related to the timing of the forecast. The proposed structure was first trained and then used in a real setting aimed to provide the French energy demand forecast using ARPEGE forecasting weather data. The results show that the performance of this approach is in the line of the performance provided by the reference RTE subscription-based service, which opens the possibility to obtain high accuracy forecasting using widely accessible deep learning techniques through open-source machine learning platforms.
ARTICLE | doi:10.20944/preprints202012.0236.v1
Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: dockless bike-sharing system; Markovian queueing network; relocation; unequal demand
Online: 9 December 2020 (16:49:27 CET)
Although the dockless bike-sharing system, which can be regarded as a typical example of the resource-sharing system, has been increasingly popular for years with people especially in China, the imbalanced distribution of shared bikes gradually becomes a major problem for both bike-sharing companies and their customers. To solve the imbalance problem, we aim to investigate the long-term performance of a system under the influence of some key factors (with an emphasis on the unequal demand between different nodes), which can guide us to discover the causes of the problem and offer several valuable suggestions to the operators. According to the fundamental principle of a dockless bike-sharing system, we propose a model reduction method to reduce the complexity of the theoretical network models, which are developed based on the Markovian queueing theory with the consideration of higher-demand nodes and lower-demand nodes. The theoretical network models provide us with steady-state probabilities of having a certain number of bikes at one node, which are used as an important part of the optimization model for solving the imbalance problem by carrying out an operator-based relocation strategy. The objective of the optimization model is to maximize the total profit and determine the optimal relocation frequency. It is found that most of the shared bikes are possible to gather at one low-demand node eventually in the long run under the influence of the different arrival rates at different nodes, but the decrease of the number of bikes at the high-demand nodes is more sensitive to the unequal demands and may cause a great loss for operators, which should be payed attention to especially when solving the relocation problems.
ARTICLE | doi:10.20944/preprints202105.0615.v1
Subject: Keywords: Building management system; Smart building; Energy consumption management; Demand response management; Energy consumption optimization
Online: 25 May 2021 (14:19:32 CEST)
Considering the increasing rate of energy consumption and its environmental detrimental effects, as well as considering the use of non-renewable energy sources such as fossil fuels, energy management issues have become more important. Given the 40% share of the building industry's total energy consumption, as well as the 80% share of energy consumed during the operation period, attention to the areas of energy management and optimization during the operation period of the buildings can have a major impact on buildings’ energy performance. In this research, through identifying building energy management tools and studying previous studies and assessing the effects of building energy management systems, the economic and environmental impacts of using building energy management systems on the annual energy consumption in an office building in Tehran as a case study has been investigated. The results indicate a 32 percent reduction in energy consumption and a significant reduction in the release of the environmental pollutants in smart mode compared to the base mode. Moreover, considering the social costs associated with the emitted pollutants as well as the return period, it has been attempted to identify the factors contributing to the economic justification of using smart heating and cooling systems. According to the results, the use of smart energy management systems can be considered as an effective step in optimizing and managing energy consumption in the construction sector.
ARTICLE | doi:10.20944/preprints202001.0008.v1
Subject: Social Sciences, Economics Keywords: theory of energy demand; empirical application; cointegration; theory-driven approach; data-driven approach
Online: 2 January 2020 (04:22:15 CET)
In this short note, the described step-by-step derivations of the industrial energy demand function from the production function framework and provided researchers with two specifications. Then we applied these theoretical specifications to the time series data as empirical analysis. We concluded that theories should be considered at the beginning of the empirical analyses but the data also should be allowed to speak freely. Hence, the main suggestion of this short note is that it would be a better strategy to consider the combination of theory-driven and data-driven approaches in the empirical analyses.
ARTICLE | doi:10.20944/preprints202209.0216.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: distributed generation; demand-side management; hybrid power system; micro grid; renewable energy resources; supply-side management
Online: 15 September 2022 (03:18:48 CEST)
Sources are classified into two depending upon the factor of reviving. These sources, which cannot be revived into their original shape once they are consumed, are considered as nonrenewable energy resources, i.e., (coal, fuel) Moreover, those energy resources which are revivable to the original condition even after being consumed are known as renewable energy resources, i.e., (wind, solar, hydel) Renewable energy is a cost-effective way to generate clean and green electrical energy Now a day’s majority of the countries are paying heed to energy generation from RES Pakistan is mostly relying on conventional energy resources which are mostly nonrenewable in nature coal, fuel is one of the major resources, and with the advent of time their prices are increasing on the other hand RES have great potential in the country with the deployment of RES greater reliability and an effective power system can be obtained In this thesis, a similar concept is being used and a hybrid power system is proposed which is composed of intermixing of renewable and nonrenewable sources The Source side is composed of solar, wind, fuel cells which will be used in an optimal manner to serve load The goal is to provide an economical, reliable, uninterruptable power supply. This is achieved by optimal controller (PI, PD, PID, FOPID) Optimization techniques are applied to the controllers to achieve the desired results. Advanced algorithms (Particle swarm optimization, Flower Pollination Algorithm) will be used to extract the desired output from the controller Detailed comparison in the form of tables and results will be provided, which will highlight the efficiency of the proposed system.
ARTICLE | doi:10.20944/preprints201910.0069.v1
Subject: Engineering, Mechanical Engineering Keywords: building energy modeling; energy systems; energy demand; future climate; weather files
Online: 7 October 2019 (12:19:24 CEST)
The building sector accounts for nearly 40% of total primary energy consumption in the U.S. and E.U. and 20% of worldwide delivered energy consumption. Climate projections predict an increase of average annual temperatures between 1.1-5.4°C by 2100. As urbanization is expected to continue increasing at a rapid pace, the energy consumption of buildings is likely to play a pivotal role in the overall energy budget. In this study we used EnergyPlus building energy models to estimate the future energy demands of commercial buildings in Salt Lake County, Utah, USA, using locally-derived climate projections. We found significant variability in the energy demand profiles when simulating the study buildings under different climate scenarios, based on the energy standard the building was designed to meet, with reductions ranging from 10% to 60% in natural gas consumption for heating and increases ranging from 10% to 30% in electricity consumption for cooling. A case study, using projected 2040 building stock, showed a weighted average decrease in heating energy of 25% and an increase of 15% in cooling energy. We also found that building standards between ASHRAE 90.1-2004 and 90.1-2016 play a comparatively smaller role than variation in climate scenarios on the energy demand variability within building types. Our findings underscore the large range of potential future building energy consumption which depend on climatic conditions, as well as building types and standards.
ARTICLE | doi:10.20944/preprints201711.0069.v1
Subject: Keywords: game theory; smart grid; energy storage; battery modelling; demand-side management; load-shaping
Online: 10 November 2017 (10:08:01 CET)
Energy storage systems will play a key role for individual users in the future smart grid. They serve two purposes: (i) handling the intermittent nature of renewable energy resources for a more reliable and efficient system, and (ii) preventing the impact of blackouts on users and allowing for more independence from the grid, while saving money through load-shifting. In this paper we investigate the latter scenario by looking at a neighbourhood of 25 households whose demand is satisfied by one utility company. Assuming the users possess lithium-ion batteries, we answer the question of how each household can make the best use of their individual storage system given a real-time pricing policy. To this end, each user is modelled as a player of a non-cooperative scheduling game. The novelty of the game lies in the advanced battery model, which incorporates charging and discharging characteristics of lithium-ion batteries. The action set for each player comprises day-ahead schedules of their respective battery usage. We analyse different user behavior and are able to obtain a realistic and applicable understanding of the potential of these systems. As a result, we show the correlation between the efficiency of the battery and the outcome of the game.
ARTICLE | doi:10.20944/preprints201808.0120.v3
Subject: Engineering, Control & Systems Engineering Keywords: HVAC model predictive control, demand response, EnergyPlus, particle swarm optimization (PSO), renewable energy, smart grids
Online: 10 September 2018 (10:58:25 CEST)
A new model predictive control (MPC) algorithm is used to select optimal air conditioning setpoints for a commercial office building, considering variable electricity prices, weather, occupancy and lighting. This algorithm, Cost-Comfort Particle Swarm Optimization (CCPSO), is the first to combine a realistic, smooth representation of occupants’ willingness to pay for thermal comfort with a bottom-up, non-linear model of the building and air conditioning system under control. We find that using a quadratic preference function for temperature can yield solutions that are both more comfortable and lower-cost than previous work that used a ``brick wall'' preference function with no preference for further cooling within an allowed temperature band and infinite aversion to going outside the allowed band. Using historical pricing data for a summer month in Chicago, CCPSO provided a 3\% reduction in costs vs. a ``brick-wall'' MPC approach with similar comfort and 13\% reduction in costs vs. a standard night setback strategy. CCPSO also reduced peak-hours demand by 3\% vs. the ``brick-wall'' strategy and 15\% vs. standard night-setback. At the same time, the CCPSO strategy increased off-peak energy consumption by 15\% vs. the ``brick-wall'' strategy. This may be valuable for power systems integrating large amounts of renewable power, which can otherwise become uneconomic due to saturation of demand during off-peak hours.
REVIEW | doi:10.20944/preprints201905.0175.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: demand prediction, energy systems; machine learning; artificial neural network (ANN); support vector machines (SVM); neuro-fuzzy; ANFIS; wavelet neural network (WNN); big data; decision tree (DT); ensemble learning; hybrid models; data science; deep learning; renewable energies; energy informatics; prediction; forecasting; energy demand
Online: 14 May 2019 (14:00:40 CEST)
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.
ARTICLE | doi:10.20944/preprints202011.0406.v1
Subject: Engineering, Automotive Engineering Keywords: energy use; demand-controlled ventilation; hybrid ventilation; humidity; multi-unit residential building; simulation; CONTAM
Online: 16 November 2020 (09:16:41 CET)
A humidity-sensitive demand-controlled ventilation system is known for many years. It has been developed and commonly applied in regions with an oceanic climate. Some attempts were made to introduce this solution in Poland in a much severe continental climate. The article evaluates this system's performance and energy consumption applied in an 8-floor multi-unit residential building, virtual reference building described by the National Energy Conservation Agency NAPE, Poland. The simulations using the computer program CONTAM were performed for the whole hating season for Warsaw's climate. Besides passive stack ventilation that worked as a reference, two versions of humidity-sensitive demand-controlled ventilation were checked. The difference between them lies in applying the additional roof fans that convert the system to hybrid. The study confirmed that the application of demand-controlled ventilation in multi-unit residential buildings in a continental climate with warm summer (Dfb) leads to significant energy savings. However, the efforts to ensure acceptable indoor air quality require hybrid ventilation, which reduces the energy benefits. It is especially visible when primary energy use is analyzed.
ARTICLE | doi:10.20944/preprints201703.0130.v1
Subject: Engineering, Energy & Fuel Technology Keywords: demand management; European Supergrid; peak loads; residential electricity demand
Online: 17 March 2017 (04:41:25 CET)
The creation of a Europe-wide electricity market combined with the increased intermittency of supply from renewable sources calls for an investigation into the risk of aggregate peak demand. This paper makes use of a risk model to assess differences in time-use data from residential end-users in five different European electricity markets. Drawing on the Multinational Time-Use Survey database, it assesses risk in relation to the probability of electrical appliance use within households for five European countries. Findings highlight in which countries and for which activities the risk of aggregate peak demand is higher and link smart home solutions (automated load control, dynamic pricing and smart appliances) to different levels of peak demand risk.
ARTICLE | doi:10.20944/preprints202012.0546.v1
Subject: Social Sciences, Accounting Keywords: classification and regression trees; CART algorithm; design thinking; web-based prototype; engagement; ICT technologies; households; water demand management (WDM); machine learning; water consumption range
Online: 22 December 2020 (09:42:57 CET)
This paper uses the numerical results of surveys sent to Huelva’s (Andalusia, Spain) households to determine the degree of knowledge they have about the urban water cycle, needs, values, and attitudes regarding water in an intermediary city with low water stress. In previous research, we achieved three different households’ clusters. The first one grouped households with high knowledge of the integral water cycle and a positive attitude to smart devices at home. The second cluster described households with low knowledge of the integral water cycle and high sensitivity to price. The third one showed average knowledge and predisposition to have a closer relationship with the water company. This paper continues with this research line, applying Classification and Regression Trees (CART) to determine which hierarchy of variables/factors/ independent components obtained from the surveys are the decisive ones to predict the range of household water consumption in Huelva. Positive attitudes towards improved cleaning habits for personal or household purposes are the highest hierarchy component to predict the water consumption range. Second in the hierarchy, the variable Knowledge Global Score about the integral urban water cycle, associated with water literacy, also contributes to predicting the water consumption range. Together with the three clusters obtained previously, these results will allow us to design water demand management strategies (WDM) fit for purpose that enable Huelva’s households to use water more efficiently.
ARTICLE | doi:10.20944/preprints202010.0156.v1
Subject: Keywords: Artificial intelligence; Deep reinforcement learning; Demand Response; Dynamic pricing; Energy management system; Microgrid; Neural networks; Price-responsive loads; Smart grid; Thermostatically controlled loads
Online: 7 October 2020 (11:21:03 CEST)
In this paper, we study the performance of various deep reinforcement learning algorithms to enhance the energy management system of a microgrid. We propose a novel microgrid model that consists of a wind turbine generator, an energy storage system, a set of thermostatically controlled loads, a set of price-responsive loads, and a connection to the main grid. The proposed energy management system is designed to coordinate among the different flexible sources by defining the priority resources, direct demand control signals, and electricity prices. Seven deep reinforcement learning algorithms were implemented and are empirically compared in this paper. The numerical results show that the deep reinforcement learning algorithms differ widely in their ability to converge to optimal policies. By adding an experience replay and a semi-deterministic training phase to the well-known asynchronous advantage actor-critic algorithm, we achieved the highest model performance as well as convergence to near-optimal policies.
ARTICLE | doi:10.20944/preprints202110.0365.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: Model predictive control; Mixed-integer linear programming; Multi-objective optimization; Energy storage management; Load management; More electric aircraft; Demand-side flexibility
Online: 25 October 2021 (15:43:38 CEST)
Abstract: Safety issues related to the electrification of more electric aircraft (MEA) need to be addressed because of the increasing complexity of aircraft electrical power systems and the growing number of safety-critical sub-systems that need to be powered. Managing the energy storage systems and the flexibility in the load-side plays an important role in preserving the system’s safety when facing an energy shortage. This paper presents a system-level centralized operation management strategy based on model predictive control (MPC) for MEA to schedule battery systems and exploit flexibility in the demand-side while satisfying time-varying operational requirements. The proposed online control strategy aims to maintain energy storage (ES) and prolong the battery life cycle, while minimizing load shedding, with fewer switching activities to improve devices lifetime and to avoid unnecessary transients. Using a mixed-integer linear programming (MILP) formulation, different objective functions are proposed to realize the control targets, with soft constraints improving the robustness of the model. Besides, an evaluation framework is proposed to analyze the effects of various objective functions and the prediction horizon on system performance, which provides the designers and users of MEA and other complex systems with new insights into operation management problem formulation.
ARTICLE | doi:10.20944/preprints202007.0409.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: Demand Side Management; Demand Response; Cyber-Physical Systems; Dynamic Pricing; Load Forecasting; Attack Detection
Online: 19 July 2020 (11:14:01 CEST)
Demand-Side Management (DSM) is an essential tool to ensure power system reliability and stability. In future smart grids, certain portions of a customer’s load usage could be under the automatic control of a cyber-enabled DSM program, which selectively schedules loads as a function of electricity prices to improve power balance and grid stability. In this scenario, the security of DSM cyberinfrastructure will be critical as advanced metering infrastructure and communication systems are susceptible to cyber-attacks. Such attacks, in the form of false data injections, can manipulate customer load profiles and cause metering chaos and energy losses in the grid. The feedback mechanism between load management on the consumer side and dynamic price schemes employed by independent system operators can further exacerbate attacks. To study how this feedback mechanism may worsen attacks in future cyber-enabled DSM programs, we propose a novel mathematical framework for (i) modeling the nonlinear relationship between load management and real-time pricing, (ii) simulating residential load data and prices, (iii) creating cyber-attacks, and (iv) detecting said attacks. In this framework, we first develop time-series forecasts to model load demand and use them as inputs to an elasticity model for the price-demand relationship in the DSM loop. This work then investigates the behavior of such a feedback loop under intentional cyber-attacks. We simulate and examine load-price data under different DSM-participation levels with three types of random additive attacks: ramp, sudden, and point attacks. We conduct two investigations for the detection of DSM attacks. The first studies a supervised learning approach, with various classification models, and the second studies the performance of parametric and nonparametric change point detectors. Results conclude that higher amounts of DSM participation can exacerbate ramp and sudden attacks leading to better detection of such attacks, especially with supervised learning classifiers. We also find that nonparametric detection outperforms parametric for smaller user pools, and random point attacks are the hardest to detect with any method.
ARTICLE | doi:10.20944/preprints201807.0084.v1
Online: 5 July 2018 (08:09:35 CEST)
The case for demand-driven research and development has received important considerations among governments, donors and programme implementing partners in development planning and implementation. Addressing demand is believed to be a bottom-top approach for designing and responding to development priorities and is good for achieving development outcomes. In this paper, we discuss the concept and application of demand driven research for development (DDRD) in Africa. We use evidence of six projects implemented under the BiomassWeb Project in Africa. We focus on parameters on level of engagement of stakeholders - whose demand is being articulated, the processes for demand articulation, capacity building and implementation processes, innovativeness of the project, reporting and sustainability of the project. We find that the nature of the institutions involved in articulation and implementation of demand-driven research and development projects and their partnerships influence the impact and reporting of demand-driven projects.
ARTICLE | doi:10.20944/preprints202105.0109.v1
Subject: Social Sciences, Economics Keywords: Electricity Markets; Integration; Demand Response; Innovation; Regulation
Online: 6 May 2021 (15:25:51 CEST)
We select four important waves of new entrants that knocked on the door of European electricity markets to illustrate how market rules need to be continuously adapted to allow new entrants to come in and push innovation forward. The new entrants that we selected are utilities venturing into neighbouring markets after establishing a strong position in their home market, utility-scale renewables project developers, asset-light software companies aggregating the assets of smaller consumers and producers, and different types of communities. We show that well-intentioned rules designed for certain types of market participants can (unintentionally) become obstacles for new entrants. We conclude that the evolution of market rules illustrates the importance of dynamic regulation. At the start of the liberalisation process the view was that we would deregulate or re-regulate the sector after which the role of regulators could be reduced. But their role has only increased. New players might also present new risks that require intervention by regulators.
CASE REPORT | doi:10.20944/preprints202001.0211.v1
Subject: Earth Sciences, Environmental Sciences Keywords: water demand; megacity wastewater; hydrological balance scenarios
Online: 19 January 2020 (04:58:48 CET)
The megacities´ sewage creates socioeconomic dependence related to water availability in the nearby zones, especially in countries with hydric stress. The present paper studies the water balance progression of realistic scenarios from 2005 to 2050 in the Mezquital Valley, the receptor of Mexico City untreated sewage since 1886, allowing agriculture irrigation in unsustainable conditions. WEAP model calculated the water demand and supply. Validation was performed with outflows data of the Tula River and simulated three scenarios: 1st) Steady-state based on inertial growth rates, 2nd) Transient scenario concerned climate change outcomes, with minor influence in surface water and hydric stress in 2050; 3rd) Transient scenario perturbed with a planned reduction of 36% in the imported wastewater and the start-up of a massive Water Treatment Plant, allowing drip and sprinkler irrigation since 2030. In the 2005-2017 period, 59% of the agriculture depended on the flood irrigation with megacity sewage. The water balance scenarios evaluated the sectorial supply of the ground and superficial water. Drip irrigation would reduce 42% of agriculture demands, but still does not grant the downflow hydroelectric requirements, aggravated by the lack of wastewater supply since 2030. This research alerts about how present policies compromise future Valley demands.
ARTICLE | doi:10.20944/preprints201902.0256.v1
Subject: Engineering, Energy & Fuel Technology Keywords: Home energy management system, Flexible demand-response, optimal load-scheduling, Mixed Integer Programming, Predictive control, demand-side-management
Online: 27 February 2019 (12:10:32 CET)
In this work, an algorithm for the scheduling of household appliances to reduce the energy cost and the peak-power consumption is proposed. The system architecture of a home energy management system (HEMS) is presented to operate the appliances. The dynamics of thermal and non-thermal appliances is represented into state-space model to formulate the scheduling task into a mixed-integer-linear-programming (MILP) optimization problem. Model predictive control (MPC) strategy is used to operate the appliances in real-time. The HEMS schedules the appliances in a dynamic manner without any a priori knowledge of the load-consumption pattern. At the same time, HEMS responds to the real-time electricity market and the external environmental conditions (solar radiation, ambient temperature etc). Simulation results exhibit the benefits of proposed HEMS by showing the reduction of up to 47% in electricity cost and up to 48% in peak power consumption.
ARTICLE | doi:10.20944/preprints201804.0056.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: demand-side management; peak demand control; dynamic-interval density forecast; stochastic optimization; dimension reduction; battery energy-storage system (BESS)
Online: 4 April 2018 (08:37:59 CEST)
A Demand-side management technique are deployed along with battery energy-storage systems (BESSs) to lower the electricity cost by mitigating the peak load of a building. Most of the existing methods rely on manual operation of the BESS, or even an elaborate building energy-management system resorting to a deterministic method that is susceptible to unforeseen growth in demand. In this study we propose a real-time optimal operating strategy for BESS based on density demand forecast and stochastic optimization. This method takes into consideration uncertainties in demand when accounting for an optimal BESS schedule, making it robust compared to the deterministic case. The proposed method is verified and tested against existing algorithms. Data obtained from a real site in South Korea is used for verification and testing. The results show that the proposed method is effective, even for the cases where the forecasted demand deviates from the observed demand
ARTICLE | doi:10.20944/preprints202007.0062.v1
Subject: Engineering, Other Keywords: Air transportation; Brazilian Amazon; Demand; Elasticity; Isolated cities
Online: 5 July 2020 (10:26:01 CEST)
The literature, aimed at understanding the income-price elasticity of air passenger demand, bases its analysis on airport movement. The diversity of studies regarding the casualty between air transportation and economic growth are examples. Some studies covering this link, estimate the income-price relationship with the demand considering international traffic. Considering a domestic setting, where this traffic is significant in Brazil, studies related to remote regions are scarce, and the existing ones focus on governmental policies and subsidies. In addition, empirical studies on the theme consenter themselves in developed regions, such as Europe, North America, and Australia. For Brazil, where we find the Amazon region, there are no empirical researches. This paper analyses the price-income elasticity of the demand regarding domestic passengers in air links from remote cities of the Brazilian Amazon. This study uses panel data regression analysis method on a database of domestic scheduled flights of Brazil´s National Civil Aviation Agency. The results show that air passengers involving remote region flights present a lower sensitiveness regarding local income and airline´s price variations than those in flights among capitals. The higher difference is in income-elasticity of the remote city of origin, which is lower than that of the air traffic among capitals.
ARTICLE | doi:10.20944/preprints201904.0132.v1
Subject: Biology, Agricultural Sciences & Agronomy Keywords: rachis; traditional; post shooting approach; economic production; demand
Online: 11 April 2019 (05:41:39 CEST)
Background and Objectives: Due to huge demand and availability of Banana, innovative cost effective method is necessary to promote and smoothen the banana production among farmers commercially mitigating the demand. Method and Materials: In this study, we feed cow dung mixture along with Urea, TSP, MoP, water to the distal part of rachis after cutting down male bud as soon as the female flowers matured into fruits (T1). The effect of this method was then compared with two control groups; one with the same strategy except fertilizer applied on root following ring method (C1, Positive control) and another was male flower untouched without applying fertilizer on rachis or root (C2, Negative control). Results and Conclusion: T1 showed more than double increase in length than controls. In the same way, in case of shape (diameter), T1 (0.46 cm) showed twice as better growth in the C1 (0.22 cm) and C2 (0.18 cm). Trend analysis showed the test group T1 curve is much steeper than the control groups suggesting faster growth rate than the other two. Finally, the cost of fertilizers for T1 per plant was estimated 0.091 USD while for positive control C1 it was 2.9 USD. This study shows an approach to be effective and economic comparing to traditional method of fertilizer application, which can be adapted as a new method of banana production.
ARTICLE | doi:10.20944/preprints201808.0383.v1
Subject: Social Sciences, Marketing Keywords: tourism demand, climate change, climate volatility, GARCH model
Online: 21 August 2018 (15:48:14 CEST)
As climate is not only a valuable tourism resource but also a factor influencing travel experience, estimating climate change can provide implications to sustainable development of the tourism industry. This study develops Climate Volatility Index (CVI) using GARCH model and estimates the relationship between CVI and Japanese tourism demand for Korea using a tourism demand model, based on data from January 2000 to December 2013. Time lag is applied based on a decision making process regarding travel destinations. The result shows that an increase in volatility of climate change leads to a decrease in tourism demand.
ARTICLE | doi:10.20944/preprints201801.0216.v1
Subject: Engineering, Energy & Fuel Technology Keywords: Electricity Demand; ANN; PSO; GA; Hybrid Optimization; Forecasting
Online: 23 January 2018 (15:30:10 CET)
In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , electricity consumption per capita, income growth rate and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015 , the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the "as-it-is" scenario , the second scenario is based on milestones set for achieving goals of "Vision 2023" document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that a direct causality exists between GSDP and the electricity demand of the state.
ARTICLE | doi:10.20944/preprints201711.0190.v2
Subject: Engineering, Energy & Fuel Technology Keywords: electricity demand; ANN; PSO; GA; hybrid optimization; forecasting
Online: 16 January 2018 (07:44:04 CET)
In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , electricity consumption per capita, income growth rate and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015, the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the “as-it-is” scenario, the second scenario is based on milestones set for achieving goals of “Vision 2023” document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that a direct causality exists between GSDP and the electricity demand of the state.
ARTICLE | doi:10.20944/preprints202206.0341.v1
Subject: Social Sciences, Education Studies Keywords: Inequality; Lorenz Curve; Education; Equity; demand and supply; Ethiopia
Online: 24 June 2022 (16:03:48 CEST)
Globally, inequalities in educational provision are prevalent between genders, various geographical regions, and among different socio-economic backgrounds. Consequently, this study set-out to assess the level of disparity among the Federal Regional States of Ethiopia using Gini-coefficient and Lorenz curve from the statistical data of MoE. Moreover, data were collected from 656 respondents found in the sample regions. The result of the Gini-coefficient indicated that disparity in educational provision has been reduced over the past couple of decades both at primary (0.145 to 0.032) and secondary levels (0.277 to 0.126). Emerging regions are by far lagging behind the central and established regions. The sources of variation were mainly the demand-side variables, especially the economic and contextual related issues like drought and conflicts. Therefore, educational policies designed at the central level are advised to consider the strategies to bridge the existing inequalities through equitable provision of the education system to its citizen.
COMMUNICATION | doi:10.20944/preprints202010.0348.v1
Online: 16 October 2020 (12:06:39 CEST)
With the year 2020, the world faced a new threat that affects all areas of life, negatively affects production in all areas, and paralyzes social life. The measures and restrictions taken by the country's governments to prevent the epidemic from spreading rapidly in the society with the effect of the Covid-19 virus, which first appeared in China and spread all over the world, brought a new lifestyle. Covid-19 has been much the impact on electricity use and electricity production in the period in Turkey as in other countries. There was a sharp decline in commercial and industrial electricity use. The coronavirus effect has also been reflected in the electricity demand and the consumption amount has undergone a great negative change. Due to the enactment of measures against the new type of coronavirus (COVID-19) epidemic and the partial or full-time curfews, electricity consumption was moved to homes, supermarkets, and hospitals in April 2020 from places where mass consumption is intense, such as industry, workplaces, and educational institutions. In this study, Covid-19 period, the first cases were examined electricity production and consumption in Turkey as of the date it is seen throughout, in comparison with electricity consumption data in the same month of the previous years corresponding to this period, the effects on electricity generation and consumption habits of this period were examined.
ARTICLE | doi:10.20944/preprints202008.0140.v1
Subject: Mathematics & Computer Science, Applied Mathematics Keywords: leadtime; demand uncertainty; revenue -sharing contract; production -marketing coordination
Online: 6 August 2020 (08:56:04 CEST)
In this paper, we consider a make-to-order supply chain which satisfies demand that is dependent on both price and quoted lead -time. The manufacturer chooses the lead -time and the order quantity, and the retailer sets the revenue shares. The interactions between the manufacturer and the retailer are modelled as a Nash Game, and the existence and uniqueness of pure strategy equilibrium are demonstrated. A mechanism that enables the supply chain to coordinate the decisions of the members is developed. Lastly, we also analyze how the supply chain system parameters impact the optimal supply chain decisions and the supply chain performance.
ARTICLE | doi:10.20944/preprints202002.0149.v1
Subject: Engineering, Civil Engineering Keywords: Water Demand; Water Supply; Performance; Hydraulic Modeling; Water GEMSV8i
Online: 11 February 2020 (14:52:17 CET)
This study was conducted generally by aiming assessment of the hydraulic performance of water distribution systems of Addis Ababa Science and Technology University (AASTU). In line with the main objective, this study addressed, (1) pinpointing problems of existing water supply versus demand deficit (2) evaluating the hydraulic performance of water distribution system using water GEMS and (3) recommended alternative methods for improving water demand scenarios. The University’s water supply distribution network layout was a looped system and the flow of water derived by both gravity and pressurized system. The gravity flow served for the academic and administrative staffs whereas the pressurized system of the network fed the students dormitories, cafeteria’s etc. The study revealed the existence of unmet minimum pressure requirement around the student dormitories which accounts 25.64% below the country’s building code standard during the peak hour consumption. The result of the water demand projection showed an increment of 2.5 liter per capita demand (LPCD) in every five years. Hence, first, the university’s water demand was projected and then hydraulic parameters such as; pressure, head loss and velocity were modeled for both the existing and the improved water supply distribution. The finding of the study was recommended to the university’s water supply project and institutional development offices for its future modification and rehabilitation works.
ARTICLE | doi:10.20944/preprints201908.0315.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: current harmonics; stray losses; statistical inference; daily demand graphs
Online: 30 August 2019 (04:16:21 CEST)
Power Electronic development determines introduction of nonlinear devices in Electric Power Systems. Introduction of nonlinear devices increase current harmonics in Transmission and Distribution Power Systems. Distribution transformers and feeders increase power losses and their nominal parameters are reduced. Present work introduces a procedure to evaluate maximum permissible load in single phase distribution transformers with massive introduction of a new type of nonlinear load which changes daily demand graphs.
ARTICLE | doi:10.20944/preprints201709.0108.v1
Subject: Mathematics & Computer Science, General & Theoretical Computer Science Keywords: peak demand minimization; job scheduling; approximation algorithms; smart grid
Online: 22 September 2017 (10:08:35 CEST)
This paper examines an important problem in smart grid energy scheduling; peaks in power demand are proportionally more expensive to generate and provision for. The issue is exacerbated in local microgrids that do not benefit from the aggregate smoothing experienced by large grids. Demand-side scheduling can reduce these peaks by taking advantage of the fact that there is often flexibility in job start times. We focus attention on the case where the jobs are non-preemptible, meaning once started, they run to completion. The associated optimization problem is called the Peak Demand Minimization problem and has been previously shown to be NP-hard. Our results include an optimal fixed-parameter tractable algorithm, a polynomial-time approximation algorithm, as well as an effective heuristic that can also be used in an online setting of the problem. Simulation results show these methods can reduce peak demand by up to 50% versus on-demand scheduling for household power jobs.
ARTICLE | doi:10.20944/preprints201709.0019.v1
Subject: Social Sciences, Other Keywords: Educational Tourism; Tourism Supply and Demand; Experience; Tourism Activities
Online: 6 September 2017 (11:58:11 CEST)
The Smart Park (well-known as Taman Pintar) as a major educational tourism destination in Jogjakarta offers a variety of tourism attractions that are very interesting for tourists. The main purpose of tourists visiting Smart Park is to get an educational tourism experience. A subjective experience raises a specific challenge for Smart Park toward the status of competitive destination. The purpose of this study is to analyze the aspects of educational tourism experience that are affected by tourism demand and supply. Data were collected by survey technique to 150 respondents and analyzed using path analysis. The results of analysis show that tourism demand and supply contributed to the variation of tourism activities by 45.1%, while the remaining was explained by other variables, such as National Budget, Local Budget, ticket sale, and cooperation with some stakeholders. Tourism supply had a higher effect than tourism demand. Tourism demand did not partially affect tourism experience. However, the results of the path analysis indicate that tourism supply had direct and indirect effects on tourism experience through the variation of tourism activities with indirect effect being dominant. In the management of Smart Park, there is still a gap between tourism demand and supply, so that the environment of tourism experience has not been created maximally.
ARTICLE | doi:10.20944/preprints201703.0231.v1
Subject: Materials Science, General Materials Science Keywords: copper resources; demand forecasting; system dynamics model; sustainability development
Online: 31 March 2017 (10:50:56 CEST)
Copper demand for a country's copper industry has a greater pull effect. China's copper consumption in 2015 has accounted for 50% of the world. The scientific forecast of China's copper demands trend is also an important basis for analyzing its future environmental impact. This paper assumes that China's economy will be developing high, medium and low scenarios, and forecasts economic and social indicators such as total GDP, population and per capita GDP in China from 2016 to 2030. Then, predicted the demand of copper resources in China from 2016 to 2030 by the combination of system dynamics model, vector autoregressive moving average model and inverted U-type empirical model. The results show that: (1) in 2020, 2025 and 2030, China's refined copper demand will be 13 Mt, 15 Mt and 15.5 Mt. (2) China's copper demand growth slowed down significantly from 2016-2030. (3) 2025-2030, China's copper resource demand is stable, into the platform of demand growth, the highest peak value in 2027 will be 15.5 Mt. (4) 2030 years later, China's copper resource demand will enter a slow decline.
ARTICLE | doi:10.20944/preprints202204.0131.v1
Subject: Behavioral Sciences, Other Keywords: Air quality; Geolife; Olympics; Traffic demand; Transport planning; Transport regulation
Online: 14 April 2022 (10:25:45 CEST)
Over the years, researchers have been studying the effect of weather and context data on the transport mode choice. The majority of these works are based on survey data, however the accuracy of their findings relies on how respondents give accurate and honest answers. In this paper, the potential of using GPS trajectories as an alternative to travel surveys in studying the impact of weather and context data on transport mode choices is investigated in Beijing city. In the analysis, we apply both descriptive and statistical models such as the MNL and MNP models. Our findings indicate that temperature has the most prominent effect among weather conditions. For instance, for temperatures greater than 25 °C, the walking share increases by 27% and the bike share reduces by 21%, which is line with the results from several survey studies. In addition, the evidence of government policy on transport regulation is revealed when the air quality becomes hazardous as people are encouraged to use environmentally friendly travel mode choices such as the bike instead of the bus and car, which are known CO2 emitters. Moreover, due to a series of traffic restrictions introduced by the Beijing government during the 2008 summer Olympics, a decrease of 17.5% in the car share and an increase of 13% and 10% in the walking and bus shares, respectively are observed. These findings provide a scientific basis for effective transport regulation and planning purposes.
Subject: Engineering, Automotive Engineering Keywords: Electricity demand forecast; Machine Learning; Artificial Neural Networks; systematic review.
Online: 21 May 2021 (09:48:10 CEST)
The forecast of electricity demand has been a recurrent research topic for decades, due to its economical and strategic relevance. Several Machine Learning (ML) techniques have evolved in parallel with the complexity of the electric grid. This paper reviews a wide selection of approaches that have used Artificial Neural Networks (ANN) to forecast electricity demand, aiming to help newcomers and experienced researchers to appraise the common practices and to detect areas where there is room for improvement in the face of the current widespread deployment of smart meters and sensors, which yields an unprecedented amount of data to work with. The review looks at the specific problems tackled by each one of the selected papers, at the results attained by their algorithms, and at the strategies followed to validate and compare the results. This way, it is possible to highlight some peculiarities and algorithm configurations that seem to consistently outperform others in specific settings.
ARTICLE | doi:10.20944/preprints202008.0721.v2
Online: 12 October 2020 (10:15:32 CEST)
Rainwater harvesting could be an optional water source to fulfil the emergency water demand in different setups. The aim was to assess if the rainwater harvesting potential for households and selected institutions were sufficient to satisfy the emergency water demand for the prevention of COVID-19 in Dilla town, Southern, Ethiopia. Rain water harvesting potential for households and selected institutions were quantified using 17 years’ worth of rainfall data from Ethiopian Metrology Agency. With an average annual rainfall of 1464 mm, households with 40 and 100 m2 roof sizes have a potential to harvest between 15.71-31.15 m3 and 41.73-82.73 m3 of water using Maximum Error Estimate. Meanwhile 7.2-39.7 m3 and 19.11-105.35 m3 of water can be harvested from the same roof sizes using Coefficient of Variation for calculation. Considering mean monthly rainfall, the health centres and Dilla University can attain 45.7% and 77% of their emergency water demand, while the rest of the selected institutions in Dilla Town can attain more than 100 % of their demand using only rainwater. Rain water can be an alternative water source for the town in the fight against COVID-19.
ARTICLE | doi:10.20944/preprints201906.0169.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: non-intrusive load monitoring; load disaggregation; linear classifier; demand response
Online: 18 June 2019 (06:06:23 CEST)
Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to decompose the whole loads in a household, which leads to low identification accuracy. In this paper, an NILM approach based on multi-feature integrated classification (MFIC) is explored, which combines some non-electrical features such as ON/OFF duration, usage frequency of appliances, and usage period to improve load differentiability. The implementation of MFIC algorithm is consistent with traditional event-based method. The uniqueness of our algorithm is that it designs an event detector based on steady-state segmentation and a linear discriminant classifier group based on multi-feature global similarity. Simulation results using an open-access dataset demonstrate the effectiveness and high accuracy of MFIC algorithm, with the state-of-the-art NILM methods as benchmarks.
ARTICLE | doi:10.20944/preprints201902.0148.v2
Subject: Earth Sciences, Atmospheric Science Keywords: drought; wildfire; drought index; fuel moisture; California; Nevada; evaporative demand
Online: 1 March 2019 (09:40:59 CET)
Relationships between drought and fire danger indices are examined to 1) incorporate fire risk information into the National Integrated Drought Information System California-Nevada Drought Early Warning System and 2) provide a baseline analysis for application of drought indices into a fire risk management framework. We analyzed four drought indices that incorporate precipitation and evaporative demand (E0) and three fire indices that reflect fuel moisture and potential fire intensity. Seasonally averaged fire danger indices were most strongly correlated to multi-scalar drought indices that use E0 (the Evaporative Demand Drought Index [EDDI] and Standardized Precipitation Evapotranspiration Index [SPEI]) at approximately annual time scales that reflect buildup of antecedent drought conditions. Results indicate that EDDI and SPEI can inform seasonal fire potential outlooks at the beginning of summer. An E0 decomposition case study of conditions prior to the Tubbs Fire in Northern California indicate high E0 (97th percentile) driven predominantly by low humidity signaled increased fire potential several days before the start of the fire. Initial use of EDDI by fire management groups during summer and fall 2018 highlights several value-added applications, including seasonal fire potential outlooks, funding fire severity level requests, and assessing set-up conditions prior to large, explosive fire cases.
ARTICLE | doi:10.20944/preprints201808.0429.v1
Subject: Engineering, Energy & Fuel Technology Keywords: Peak Shaving; Demand Response; Block of Buildings; Thermal Model; TEASER
Online: 24 August 2018 (09:09:12 CEST)
This paper investigates how blocks of buildings could fit into load shedding strategies. It focuses in particular on what could be the effects on peak shaving, occupants’ thermal comfort or CO2 emissions reduction and how to quickly quantify them. To achieve this goal, we focused on a new residential district, thermally fed by heat pumps. Four modeling approaches were confronted in order to estimate buildings' responses to load shedding orders. On the one hand, a quick estimation of the peak shaving impact can rely on experimental results if the buildings' envelope and uses of the experimentation match those of the study case. On the other hand, thermal simulation models allow us to assess thermal comfort while considering the building physical response. Finally, a hybrid modeling approach can provide a good compromise between modeling rapidity and accuracy of the impacts estimation. At district scale, it may be necessary to mix modeling approaches, from experimental results to detailed thermal models. Accuracy is not guaranteed for all approaches so that the choice should be made carefully in regards to study needs. However, results are sufficient to compare the effects of load shedding strategies on peak shaving, thermal comfort, and CO2 emissions reductions.
ARTICLE | doi:10.20944/preprints201712.0073.v1
Subject: Social Sciences, Economics Keywords: demand-led growth; downshifting; Kaleckian-Harrodian; post-Keynesian; ecological economics
Online: 12 December 2017 (08:34:41 CET)
If the world’s countries seriously tackle the climate targets agreed in Paris, their citizens are likely to experience substantial changes in production, consumption and employment. We present a long-run post-Keynesian model for studying the potential implications of a major transition on macroeconomic stability and employment. It is a demand-led model in which firms have considerable but not absolute freedom to administer prices, while household consumption exhibits inertia. Firms continually seek input-saving technological improvements that, in the aggregate, tie technological progress to firms' cost structure. Together with firm pricing strategies and wage setting, the productivities of different inputs determine the functional income distribution. Saving and investment, and production and purchase of consumption goods, are undertaken by different economic actors, driven by income and capacity utilization, with the possibility that productive capacity exceeds, or falls short of, effective demand. The model produces business cycles and long waves driven by technological change. We present results for a “downshifting” scenario in which households voluntarily withdraw labor and discuss the implications of downshifting for stability, growth, and employment. We contrast the downshifting scenario with ones in which households reduce consumption without withdrawing from the labor pool.
Subject: Engineering, Civil Engineering Keywords: air conditioning group load; grid friendly; active demand; storage; coordinated control
Online: 9 September 2020 (09:31:05 CEST)
The growing number of the accessed energy-eﬀicient frequency conversion air conditioners is likely to generate a large number of harmonics on the power grid. The following shortage in the reactive power of peak load may trigger voltage collapse. Hence, this conflicts with people’s expectations for a cozy environment. Concerning the problems mentioned above, an active management scheme is put forward to balance the electricity use and the normal operation of air conditioning systems. To be specific, schemes to suppress the low voltage ride through (LVRT) and harmonic are designed firstly. Then to deaden the adverse effects caused by nonlinear group load running on the grid, and to prevent the unexpected accidents engendered from grid malfunction, the dynamic sensing information obtained by an online monitor is analyzed, which can be seen as an actively supervise mechanism. The combined application of active and passive filtering technology is studied as well. Thirdly, the new energy storage is accessed reliably to cope with peak-cutting or grid breaking emergencies, and the fuzzy control algorithm is researched. Finally, system feasibility is verified by functional modules co-operation simulation, and active management target is achieved under scientific and reasonable state-of-charge (SOC) management strategy.
ARTICLE | doi:10.20944/preprints202008.0271.v1
Subject: Earth Sciences, Geoinformatics Keywords: geographic information system; land demand; land use; universal soil loss erosion
Online: 12 August 2020 (05:09:55 CEST)
The information on the land use and soil conservation practice based on year 2006, 2010 and 2014, hence offering an opportunity to model the impacts of land use change on erosion, deposition and surface water runoff. Limitation in the use of hydrological models had been their inability to handle the large amount of input data that describe the heterogeneity of the natural system. In this study, a procedure that takes into account soil conservation practice based on the land use change, the response of soil erosion and sediment export from the George Town Conurbation catchment area, and average annual sediment yields were estimated for each grid cell of the watershed to identify the critical erosion areas of rural and urban planning proposes. Average annual sediment yield and data on a grid basis estimated using Universal Soil Loss Equation (USLE) and an emerging technology represented by Geographic Information System (GIS) used as a tool to produce a map for erosion rate. The changing of the land use from forest to agriculture and then to an urban area is a challenging task to research on land use demand for population, and environmental impact assessment is important for the planning of natural resources management, allowing research the modification of land use properly and implement more sustainable for long term management strategies. The challenge is to formulate strategies that would promote an integrated approach to the land use planning at an appropriate level as to address the issues that arose. Modelling for creating urban growth boundary for the George Town Conurbation must have to be controlled surface runoff and soil loss and sediment export from land use of the George Town Conurbation catchment.
ARTICLE | doi:10.20944/preprints201808.0424.v1
Subject: Engineering, General Engineering Keywords: aggregator; demand response; distributed energy resource; information communication technology; SWOT; PEST
Online: 24 August 2018 (05:27:20 CEST)
The world is progressing towards a more advanced society where end-consumers have access to local renewable-based generation and advanced forms of information and technology. Hence, it is in a current state of transition between the traditional approach to power generation and distribution, where end-consumers of electricity have typically been inactive in their involvement with energy markets and a new approach that integrates their active participation. This new approach includes the use of distributed energy resources (DER) such as renewable-based generations and demand response (DR), which are being rapidly adopted by end-consumers, where incentives are strong. This paper presents the role of DR aggregator to effectively integrate DER technologies as a new source of energy capacity, into the electricity networks using information communication technology and industry knowledge. This framework based on DR aggregators will facilitate renewable energy integration and customer engagement in electricity market efficiently. To this aim, advantages and disadvantages of DR aggregators are discussed in this paper from political, economic, social and technological (PEST) point of views. Based on this analysis, a strengths, weaknesses, opportunities, and threats (SWOT) analysis for a typical DR aggregator is presented.
ARTICLE | doi:10.20944/preprints202104.0120.v1
Subject: Earth Sciences, Environmental Sciences Keywords: Pre-COVID-19; Post-COVID-19; Secondary Schools; Water Demand; Groundwater; Nigeria
Online: 5 April 2021 (12:22:37 CEST)
The prevalence of corona virus and the novel COVID-19 disease in the entire globe has exacerbated different impact on socioeconomic spectrum in the world, including water use pattern. Thus a research was conducted to examine the comparative use of water during pre- and post-COVID-19 lockdown pattern among post-primary schools in Iwo, Osun State, Nigeria. A survey was conducted among fifteen schools which were randomly selected, but with eight public and seven private schools for the investigation. Both descriptive and inferential statistical techniques were used in data analysis. The results revealed that the major source of water to the schools investigated is ground water which is obtained through hand-dug wells and boreholes. It was further discovered that there was increase in water use during post-COVID-19 lockdown era as a result of the directive by the government that clean water should be provided for hand-washing by all schools regardless of the owner to curtail the spread of COVID-19 disease in the country. One sample t-test also revealed that there was a significant difference in water use at (p<0.01) level. It is recommended that the government and other stakeholders in water sector to ensure that all-time and non-seasonal dependent source of water be provided rather than ground water source which is susceptible to variations in water yields from seasonal variations. This will enable continuous clean water supply, for all purposes, including COVID-19 protocols.
ARTICLE | doi:10.20944/preprints202110.0182.v1
Subject: Engineering, Energy & Fuel Technology Keywords: Electricity peak load; Taoussa’s energy sources; Long-term electricity demand planning; Scenarios simulation
Online: 12 October 2021 (12:53:37 CEST)
A long-term forecast study on the electricity demand of Taoussa of Mali is conducted in this paper, with various scenarios of socioeconomic and technological conditions. The analysis tool, which is applied in scenarios simulation, is the Model for Analysis of Energy Demand from the International Atomic Energy Agency. The analysis results are annual electricity demand and peak load forecast for the electrification from the period 2020 to 2035. During the planning period, the analysis results show that the electricity demand will increase to 49.40 MW (332.57 GWh) for the low scenario (LS), 66.46 MW (472.61 GWh) for the reference scenario (RS), and 89.47 MW (635 GWh) for the high scenario (HS). In addition, the total electricity demand increased at an average rate of 8.13% in the LS, 10.31% in the RS and 12.56% in the HS in all sectors. The electricity peak demand is expected to grow at 7.92%, 10.53% and 12.91% corresponding to the three scenarios; in this case, the system peak demand in 2035 will increase to 64.88 MW for the LS, 92.2 MW for the RS and 126.22 MW, the days of peak load are between 17th -23rd in May. The Industry sector will be the biggest electricity consumer of Taoussa area.
ARTICLE | doi:10.20944/preprints202101.0201.v1
Subject: Keywords: Public Housing; Housing Affordability; Global Cities; Subsidized Ownership; Demand-Side Policies; Social-Welfare
Online: 11 January 2021 (14:35:49 CET)
Affordable Housing, the basic human necessity has now become a critical problem in global cities with direct impacts on people's well-being. While a well-functioning housing market may augment the economic efficiency and productivity of a city, it may trigger housing affordability issues leading crucial economic and political crises side by side if not handled properly. In global cities e.g. Singapore and Hong Kong where affordable housing for all has become one of the greatest concerns of the Government, this issue can be tackled capably by the provision of public housing. In Singapore, nearly 90% of the total population lives in public housing including public rental and subsidized ownership, whereas the figure tally only about 45% in Hong Kong. Hence this study is an effort to scrutinizing the key drivers of success in affordable public housing through following a qualitative case study based research methodological approach to present successful experience and insight from different socio-economic and geo-political context. As a major intervention, this research has clinched that, housing affordability should be backed up by demand-side policies aiming to help occupants and proprietors to grow financial capacity e.g. subsidized rental and subsidized ownership can be an integral part of the public housing system to improve housing affordability.
ARTICLE | doi:10.20944/preprints201901.0060.v1
Subject: Medicine & Pharmacology, Other Keywords: rehabilitation; global health; disability; global burden of disease; health services needs and demand
Online: 8 January 2019 (10:52:39 CET)
Background: To inform global health policies and resources planning, this paper analyzes evolving trends in physical rehabilitation needs, using data on Years Lived with Disability (YLDs) from the Global Burden of Disease Study (GBD) 2017. Methods: Secondary analysis of how YLDs from conditions amenable to physical rehabilitation have evolved from 1990 to 2017, for the world and across countries of varying income levels. Linear regression analyses were used. Results: A 66.2% growth was found in estimated YLD Counts amenable to physical rehabilitation: a significant and linear growth of more than 5.1 billion YLDs per year (99%CI: 4.8–5.4; r2 = 0.99). Low-income countries more than doubled (111.5% growth) their YLD Counts amenable to physical rehabilitation since 1990. YLD Rates per 100,000 people and the percentage of YLDs amenable to physical rehabilitation also grew significantly over time, across locations (all p > 0.05). Finally, only in high-income countries Age-standardized YLD Rates significantly decreased (p < 0.01; r² = 0.86). Conclusions: Physical rehabilitation needs have been growing significantly in absolute, per-capita and in percentage of total YLDs, globally and across countries of varying income level. In absolute terms, growths were higher in lower income countries, wherein rehabilitation is under-resourced.
ARTICLE | doi:10.20944/preprints201801.0283.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: fuzzy linear programming, direct load control, scheduling optimization, chillers, air condition, demand response
Online: 30 January 2018 (13:12:58 CET)
A real-time two-way direct load control (TWDLC) of central air-conditioning chillers in wide area is proposed to provide demand response. The proposed TWDLC scheme is designed to optimize the load shedding ratio of every customer under control to ensure the target load to be shed is met at every scheduling period. In order to overcome the load reduction uncertainties of TWDLC, an innovative solution is proposed by applying a certain degree of loosening on the constraint of the actual shed load. Fuzzy linear programming is utilized to solve the optimization problem with fuzzy constraints. The proposed fuzzy linear programming problem is solved by delicately transforming it into a regular liner programming problem. A selection scheme used to obtain the feasible candidates set for load shedding at every sampling interval of TWDLC is also designed along with the fuzzy linear programming.
ARTICLE | doi:10.20944/preprints201801.0127.v1
Subject: Behavioral Sciences, Applied Psychology Keywords: Cardiovascular Disease; Psychosocial Safety Climate; Demand-Control; Effort-Reward Imbalance; Epidemiology; Psychosocial Risks
Online: 15 January 2018 (16:58:20 CET)
Abstract: Cardiovascular Disease (CVD) is the most prevalent disease worldwide, which has been linked to work stress because of poor job design as explained by the Job Demand-Control (JDC) and the Effort-Reward Imbalance (ERI) models. In this paper we explore for the first time relative impact of a specific aspect of organisational climate, Psychosocial Safety Climate (PSC), on any CVD including angina, myocardial infarction, hypertension, and stroke. We used two waves of interview data from Australia, with an average lag of 5 years (excluding baseline CVD, final n = 1223). Logistic regression was conducted to estimate the prospective associations between PSC at baseline on incident CVD at follow-up. It was found that participants in low PSC environments were 59% more likely to develop new CVD than those in high PSC environments. Logistic regression showed that PSC at baseline predicts lower CVD risk at follow-up (OR = 0.98, 95% CI 0.96-1.00), and this risk remained unchanged even after joint adjustment for measures of ERI and JDC. These results suggest that PSC is an independent risk factor for CVD in Australia. Beyond job design this study implicates organisational climate and prevailing management values regarding worker psychological health as the genesis of CVD.
ARTICLE | doi:10.20944/preprints202109.0357.v1
Subject: Engineering, Biomedical & Chemical Engineering Keywords: Biofabrication; Bioprinting; Drop-on-demand; Microvalve; Micro-tissue; 3D Cell culture; Autologous Chondrocyte Implantation
Online: 21 September 2021 (11:16:07 CEST)
Recent improvements within the fields of high-throughput screening and 3D tissue culture have provided the possibility of developing in vitro micro-tissue models that can be used to study diseases and screen potential new therapies. This paper reports a proof of concept study on the use of microvalve-based bioprinting to create laminar MSC-chondrocyte co-cultures as an in vitro model of autologous chondrocyte implantation (ACI), an established cellular therapy for osteoarthritis. Microvalve-based bioprinting uses microvalves to deposit cells suspended in a liquid in a consistent and repeatable manner. In this case MSCs and chondrocytes have been sequentially deposited into an insert based transwell system in order to create a laminar co-culture, with variations in the ratios of the cell types used to investigate the potential for MSCs to stimulate improved repair. Histological and indirect immunofluorescence staining revealed the formation of dense tissue structures within the chondrocyte and MSC-chondrocyte cell co-cultures, alongside the establishment of a proliferative region at the base of the tissue. No stimulatory or inhibitory effect in terms of ECM production was observed through the introduction of MSCs, although the potential for an immunomodulatory benefit remains. This proof-of-concept study therefore provides a novel method to enable the scalable production of therapeutically relevant micro-tissue models that can be used for in vitro research to optimise ACI procedures.
ARTICLE | doi:10.20944/preprints202011.0205.v1
Subject: Engineering, Other Keywords: Neural Networks; Long-Short Term Model; Water demand; Forecasting; Sustainable development goals; Water Goal.
Online: 5 November 2020 (10:17:30 CET)
Climate change has become the greatest threat to the survival of world and its ecosystem. With the irreversible impact on the ecosystem, problems like rise in sea level, food-insecurity, natural resources scarcity, seasonal disorders have increased over the past few years. Among these problems, the issue of water scarcity due to the lack of water resources and global warming has plagued several nations. Owing to the rising concerns over water scarcity United Nations (UN) has acknowledged water as a primary resource to the development of societies under the ‘Water Goal’ of the sustainable development goals. As the changing climate and intermittent availability of water resources pose major challenges to forecast demand, especially in countries like the United Arab Emirates (UAE) which has one of the highest per capita residential water consumption rates in the world. Therefore, the aim of this study is to propose an accurate water demand forecasting technique that incorporates all significant factors to predict the future water demands of the UAE. The forecasting model used is the Long Short Term Memory (LSTM), with the factors considered are mean temperature, mean rainfall, relative humidity, Gross Domestic Product (GDP), Consumer Price Index (CPI) and population growth. The LSTM model predicts the water demand forecasting in the UAE showing that the future demand will decrease from 1821 million m3 in 2018 to 1809.9 million m3 in 2027.
ARTICLE | doi:10.20944/preprints201909.0223.v1
Subject: Social Sciences, Other Keywords: peer-to-peer energy trading; consumer demand; distributed ledger technology; blockchain; online survey experiment
Online: 19 September 2019 (11:30:53 CEST)
Peer-to-peer (P2P) energy trading could help address grid management challenges in a decentralizing electricity system, as well as providing other social and environmental benefits. Many existing and proposed trading schemes are enabled by blockchain, a distributed ledger technology (DLT) relying on cryptographic proof of ownership rather than human intermediaries to establish energy transactions. This study used an online survey experiment (n=2064) to investigate how consumer demand for blockchain-enabled peer-to-peer energy trading schemes in the United Kingdom varies depending on how the consumer proposition is designed and communicated. The analysis provides some evidence of a preference for schemes offering to meet a higher proportion of participants’ energy needs, and for those operating at the city/region (as compared to national or neighbourhood) level. People were more likely to say they would participate when the scheme was framed as being run by their local council, followed by an energy supplier, community energy organization, and social media company. Anonymity was the most valued DLT characteristic and mentioning blockchain’s association with Bitcoin led to a substantial decrease in intended uptake. We highlight a range of important questions and implications suggested by these findings for the introduction and operation of P2P trading schemes.
ARTICLE | doi:10.20944/preprints201901.0007.v3
Subject: Engineering, Civil Engineering Keywords: uncertain water demand; scaling laws; scenario generation; scenario reduction; water distribution networks; hydraulic simulation
Online: 11 February 2019 (11:33:12 CET)
A numerical approach for generating a limited number of water demand scenarios and estimating their occurrence probabilities in a Water Distribution Network (WDN) is proposed. This approach makes use of the demand scaling laws in order to consider the natural variability and spatial correlation of nodal consumptions. The scaling laws are employed to determine the statistics of nodal consumption as a function of the number of users and the main statistical features of the unitary user's demand. Besides, consumption at each node is considered to follow a Gamma probability distribution. A high number of groups of cross-correlated demands, i.e., scenarios, for the entire network were generated using Latin Hypercube Sampling (LHS) and the numerical procedure proposed by Iman and Conover. The Kantorovich distance is used to reduce the number of scenarios and estimate their corresponding probabilities, while keeping the statistical information on nodal consumptions. By hydraulic simulation, the whole number of generated demand scenarios was used to obtain a corresponding number of pressure scenarios on which the same reduction procedure was applied. The probabilities of the reduced scenarios of pressure were compared with the corresponding probabilities of demand.
ARTICLE | doi:10.20944/preprints201806.0254.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: demand-side management; peak demand control; dynamic-interval density forecast; stochastic optimization; dimension reduction; battery energy-storage system (BESS), plugged-in electric vehicles (PEV); vehicle-to-grid (V2G); building energy-management systems (BEMS)
Online: 15 June 2018 (13:01:42 CEST)
This study purposes the use of plug-in electric vehicles for demand side management (DSM) considering uncertainties in demand as well as uncertainties due to mobility of PEV to mitigate peak demand. The solution also seeks to reduce electric cost in addition to reducing the effects of greenhouse gases. In recent years DSM using distributed storage system such as battery energy management system (BESS) and plugged-in electric vehicles (PEV) have become very prevalent with most implementations resorting to deterministic load forecast. These methods do not consider the potential growth in demand making their solutions less robust. In this study we propose a real-time density demand forecast and stochastic optimization for robust operation of PEV for a building. This method accounts for demand uncertainties in addition to uncertainties in mobile energy storage as found in PEV, making the resulting solution robust as compared to the deterministic case. A case study on a real site in South Korea is used for verification and testing. The proposed study is verified and tested against existing algorithms. The result verifies the effectiveness of the proposed approach
ARTICLE | doi:10.20944/preprints202204.0073.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Explainable Artificial Intelligence; Human-Centric Artificial Intelligence; Smart Manufacturing; Demand Forecasting; Industry 4.0; Industry 5.0
Online: 8 April 2022 (08:11:56 CEST)
Artificial Intelligence models are increasingly used in manufacturing to inform decision-making. Responsible decision-making requires accurate forecasts and an understanding of the models’ behavior. Furthermore, the insights into models’ rationale can be enriched with domain knowledge. This research builds explanations considering feature rankings for a particular forecast, enriching them with media news entries, datasets’ metadata, and entries from the Google Knowledge Graph. We compare two approaches (embeddings-based and semantic-based) on a real-world use case regarding demand forecasting.
Subject: Engineering, Marine Engineering Keywords: part transportation; Takagi-Sugeno fuzzy control; carrier aircraft; transportation time; stochastic demand; cross rule group
Online: 15 April 2021 (15:00:28 CEST)
The part transportation efficiency is a main factor of aircraft sortie generation rate. Part transportation is used to transport spare part from base to carrier. Transportation strategy depends on both demand on carrier and inventory in transportation base. The transportation time and stochastic demand will induce fluctuations of cost and inventory. Thus, a Takagi-Sugeno fuzzy system of dynamic part transportation is established considering transportation time and stochastic demand. And a novel Takagi-Sugeno fuzzy robust control is designed for dynamic part transportation, which will keep transportation cost and part inventory stable. First of all, a fuzzy model with stochastic demand and transportation time is proposed. Then, a novel robust control with cross rule groups is conducted according to production and transportation strategy, which will reduce fluctuations induced by strategies switch. Moreover, robust stability is guaranteed and part can be supplied in time under a low cost. Finally, simulation illustrates usefulness and quickness of the novel Takagi-Sugeno fuzzy robust control. Besides, the proposed method will be useful in other transportation electrification systems with delay time and uncertainty.
REVIEW | doi:10.20944/preprints201812.0217.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: Intelligent Load Forecasting; Demand-Side Management; Pattern Similarity; Hierarchical Forecasting; Feature Selection; Weather Station Selection
Online: 18 December 2018 (10:38:10 CET)
Electricity demand forecasting has been a real challenge for power system scheduling in the different levels of the energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for load forecasting; although, scant evidence is available about the feasibility of each of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationale behind intelligent forecasting methods, based on the work of previous researchers in the field of energy. The fundamental benefits and main drawbacks of the aforementioned methods are discussed in order to depict the efficiency of each approach in various situations. In the end, a proposed hybrid strategy is represented.
ARTICLE | doi:10.20944/preprints201809.0594.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: adaptive neuro-fuzzy inference system; battery energy storage; photovoltaic unit; power demand; peak power curtailment
Online: 30 September 2018 (04:56:58 CEST)
One of the most crucial and economically beneficial tasks for energy customer is peak load curtailment. On account of the fast response of renewable energy resources (RERs) such as photovoltaic (PV) units and battery energy storage system (BESS), this task is closer to be efficiently implemented. Depends on the customer peak load demand and energy characteristics, the feasibility of this strategy may warry. When adaptive neuro-fuzzy inference system (ANFIS) is exploited for forecasting, it can provide many benefits to address the above-mentioned issues and facilitate its easy implementation, with short calculating time and re-trainability. This paper introduces a data driven forecasting method based on fuzzy logic for optimized peak load reduction. First, the amount of energy generated by PV is forecasted using ANFIS which conducts output trend, and then, the BESS capacity is calculated according to the forecasted results. The trend of the load power is then decomposed in Cartesian plane into two parts, left and right from load peak, searching for BESS capacity equal. Network switching sequence over consumption is provided by a fuzzy logic controller (FLC) with respect to BESS capacity and PV energy output. Finally, to prove the effectiveness of the proposed ANFIS-based peak shaving method, offline digital time-domain simulations have been performed on a real-life practical test micro grid system in MATLAB/Simulink environment and the results have been experimentally verified by testing on a practical micro grid system with real-life data obtained from smart meter and also, compared with several previously-reported methods.
ARTICLE | doi:10.20944/preprints202110.0090.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: artificial intelligence; machine learning; active learning; knowledge acquisition; explainable artificial intelligence; manufacturing; demand forecasting; smart assistant
Online: 5 October 2021 (15:23:46 CEST)
This research work describes an architecture for building a system that guide a user from a forecast generated by a machine learning model through a sequence of decision-making steps. The system is demonstrated in manufacturing demand forecasting use case and can be extended to other domains. In addition, the system provides means for knowledge acquisition by gathering data from users. Finally, it implements an active learning component and compares multiple strategies to recommend media news to the user. Such media news aims to provide additional context to demand forecasts and enhance judgment on decision-making.
ARTICLE | doi:10.20944/preprints202101.0375.v1
Subject: Social Sciences, Business And Administrative Sciences Keywords: cold chain logistics of agricultural products; demand forecast; principal component analysis, multiple linear regression, neural network.
Online: 19 January 2021 (11:50:09 CET)
Cold chain logistics of Agricultural Products demand forecasting can provide the scientific basis for the country to formulate logistics strategy, which further promotes the development of social economy and the improvement of living standards in China. In this paper, a new mathematical combined model is proposed to Agricultural Products Demand. Shandong, one of a China’s province, serves as the main producer and distributor of agricultural products. Based on the index system created from multiple related factors influencing cold chain logistics demand of agricultural products in Shandong, this paper employs principal component analysis to reduce the dimension of various indexes and predicts principal components with time series. Thereafter, multiple linear regression model and neural network model were constructed to forecast the cold chain logistics demand of agricultural products in Shandong, and their combined forecast models were compared. What's more, the paper provides insight for reference and decision-making concerning the development of cold chain logistics industry of agricultural products in Shandong province.
REVIEW | doi:10.20944/preprints202009.0549.v1
Subject: Engineering, Civil Engineering Keywords: domestic water demand; pond harvesting system; roof harvesting system; rainwater harvesting system; water scarcity; stormwater management
Online: 23 September 2020 (10:19:59 CEST)
This paper reviews the design and component of two types of RWHS, namely roof harvesting system (RHS) and pond harvesting system (PHS). The performance in terms of quantity and quality of collected rainwater and energy consumption for RWHS with different capacities were evaluated, as well as the benefits and challenges particularly in environmental, economic and social aspects. Presently, RHS is more commonly applied but its effectiveness is limited by its small scale. The PHS is of larger scale and has greater potentials and effectiveness as an alternative water supply system. Results also indicate the many advantages of PHS especially in terms of economics, environmental aspects and volume of water harvested. While RHS may be suited to individual or existing buildings, PHS has greater potentials and should be applied in newly developed urban areas with wet equatorial climate.
ARTICLE | doi:10.20944/preprints201912.0102.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: forecasting of fuel demand; ship’s fuel consumption; data fitting; statistical quality measures; signal processing and analysis
Online: 8 December 2019 (17:05:03 CET)
Real data obtained from ship in operation are processed and analyzed in this paper. The intention was to provide software which would predict ship’s fuel consumption in some future time instant. It is showed that it is possible to develop such software based on numeric fitting of known data. In order to check how well the prediction of future fuel consumption is, we used only the first half of data for obtaining prediction curve. The second part of data was used to compare different prediction curves goodness. Hence, the presented research used actually a “real future data” and forecasted future data, which are used to numerically evaluate goodness of prediction. The research is of interest for companies logistics, to provide adequate fuel for fleet when and where actually needed. It is concluded that there are several prediction functions which satisfy used statistical quality measures.
ARTICLE | doi:10.20944/preprints201902.0036.v1
Subject: Engineering, Biomedical & Chemical Engineering Keywords: Anaerobic digestion; Chemical oxygen demand; Optimization; Poultry slaughterhouse wastewater; Response surface methodology; Static Granular Bed Reactor
Online: 4 February 2019 (13:53:16 CET)
In this study, the efficiency of an anaerobic treatment system for wastewater from a South African poultry slaughterhouse was evaluated using a lab-scale static granular bed reactor (SGBR). The down-flow SGBR (2 L) was operated continuously for 138 days under mesophilic conditions (35-37 ˚C), at hydraulic retention times (HRTs) ranging from 24 to 96 h and average organic loading rates (OLRs) of 0.78 to 5.74 g COD/L.day. The SGBR achieved an average chemical oxygen demand (COD) removal efficiency of 80% and the maximum COD removal achieved was 95%, at an HRT of 24 h and average OLR of 5.74 g COD/L.day. The optimization of the SGBR, with regard to a suitable HRT and OLR, was determined using response surface methodology (RSM). The optimal SGBR performance with regard to the maximum COD removal efficiency was predicted for an OLR of 12.49 g COD/L.day and a HRT of 24 h, resulting in a 95.5% COD removal efficiency. The model R2 of 0.9638 indicated that the model is a good fit and is suitable to predict the COD removal efficiency for the SGBR.
ARTICLE | doi:10.20944/preprints201811.0125.v1
Subject: Social Sciences, Econometrics & Statistics Keywords: Retailers’ Optimal Pricing Strategy; Expected Utility Theory (EUT); Regret Theory; Regret Reference Point; Price-dependent Demand
Online: 5 November 2018 (15:46:20 CET)
Based on the Expected Utility Theory and Regret Theory, the Extended Regret Theory (ERT) is proposed in this paper to study the optimal pricing strategy of retailers in e-commerce environment. Taking the diversity of sales channels and the uncertainty of consumers in e-commerce environment into consideration, author of the paper designs an extended regret utility function which comprehensively considers both pessimistic and optimistic attitudes of decision makers in retailing industry to describe their regret-avoidance behavior. According to the sensitivity analysis, it is found that the optimal retail price decreases as the consumer price sensitivity coefficient increases, yet does not show variation with changes of the consumers pessimism degree. Moreover, the optimal retail price(s) obtained under EUT, ERT and combination of EUT and ERT represent the same.
ARTICLE | doi:10.20944/preprints201804.0303.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: multi-objective optimization; optimal configuration; improved gravitational search algorithm (IGSA); wind-solar-battery system; demand response
Online: 24 April 2018 (04:05:05 CEST)
This study presents application of demand response strategy in a standalone wind-solar-battery hybrid energy system (HES). Inputs for the designed HES are wind speed, solar radiation, temperature and load demand which is variable with time. In this study, hourly values of meteorological data and hourly load demand are considered in one year. An improved gravitational search algorithm (IGSA) is used to optimize the configuration of the standalone wind-solar-battery hybrid power system. The optimal objectives of the system are cost of the system in life cycle, the loss of power supply probability（LPSP）and the energy excess percentage（EXC）.The effect of demand response on economic benefit and energy storage allocation of the standalone wind-solar-battery system is studied. The obtained optimal configuration of the proposed HES can provide minimal energy cost with excellent performance and reduced waste and unmet load.
ARTICLE | doi:10.20944/preprints202104.0697.v1
Subject: Earth Sciences, Atmospheric Science Keywords: heating degree-day (HDD), cooling degree-day (CDD), climate change, projections, energy demand of residential buildings, Portugal
Online: 26 April 2021 (21:18:06 CEST)
Climate change is expected to influence cooling and heating energy demand of residential buildings and affect overall thermal comfort. Towards this end, the heating degree-day (HDD), the cooling degree-day (CDD) and the HDD+CDD were computed from an ensemble of 7 high-resolution bias-corrected simulations attained from EURO-CORDEX under RCP4.5 and RCP8.5. These three indicators were analyzed for 1971-2000 (from E-OBS) and 2011-2040 and 2041-2070, under both RCPs. Results show that the overall spatial distribution of HDD trends for the 3 time-periods points out an increase of energy demand to heat internal environments in Portugal's northern-eastern regions, most significant under RCP8.5. It is projected an increase of CDD values for both scenarios; however, statistically significant linear trends were only found for 2041-2070 under RCP4.5. The need for cooling is almost negligible for the remaining periods, though linear trend values are still considerably higher for 2041-2070 under RCP8.5. By the end of 2070, higher amplitudes for all indicators are depicted for southern Algarve and Alentejo regions, mainly under RCP8.5. For 2041-2070 the Centre and Alentejo (North and Centre) regions present major positive differences for HDD(CDD) under RCP4.5(RCP8.5), within the 5 NUTS II regions predicting higher heating(cooling) requirements for some locations.
ARTICLE | doi:10.20944/preprints201901.0146.v1
Subject: Engineering, Control & Systems Engineering Keywords: Biochemical oxygen demand (BOD); Cuckoo search algorithm (CSA); Extreme learning machine (ELM); Soft sensor; Wastewater treatment process
Online: 15 January 2019 (09:13:22 CET)
It is difficult to capture the real-time online measurement data for biochemical oxygen demand (BOD) in wastewater treatment processes. An optimized extreme learning machine (ELM) based on an improved cuckoo search algorithm (ICS) is proposed in this paper for the design of soft BOD measurement model. In ICS-ELM, the input weights matrices of the extreme learning machine (ELM) and the threshold of the hidden layer are encoded as the cuckoo's nest locations. The best input weights matrices and threshold are obtained by using the strong global search ability of improved cuckoo search (ICS) algorithm. The optimal results can be used to improve the precision of forecasting based on less number of neurons of the hidden layer in ELM. Simulation results show that the soft sensor model has good real-time performance, high prediction accuracy and stronger generalization performance for BOD measurement of the effluent quality compared to other modeling methods such as back propagation (BP) network in most cases.
ARTICLE | doi:10.20944/preprints202109.0016.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: Chance constraint programming; Source-load systems; Demand response control; Thermostatically controlled loads (TCLs); Plug-in electric vehicles (PEVs).
Online: 1 September 2021 (12:25:48 CEST)
Demand response flexible loads can provide fast regulation and ancillary services as reserve capacity in power systems. This paper proposes a joint optimization dispatch control strategy for source-load system with stochastic renewable power injection and flexible thermostatically controlled loads (TCLs) and plug-in electric vehicles (PEVs). Specifically, the optimization model is characterized by a chance constraint look-ahead programming to maximal the social welfare of both units and load agents. By solving the chance constraint optimization with sample average approximation (SAA) method, the optimal power scheduling for units and TCL/PEV agents can be obtained. Secondly, two demand response control algorithms for TCLs and PEVs are proposed respectively based on the aggregate control models of the load agents. The TCLs are controlled by its temperature setpoints and PEVs are controlled by its charging power such that the DR control objective can be fulfilled. The effectiveness of the proposed dispatch and control algorithm has been demonstrated by the simulation studies on a modified IEEE 39 bus system with a wind farm, a photovoltaic power station, two TCL agents and two PEV agents.
ARTICLE | doi:10.20944/preprints201912.0148.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: Quality of Experience; Quality of Service; QoE evaluation video on demand; Quality of Service; QoS correlation; subjective testing
Online: 11 December 2019 (04:46:57 CET)
In addition to the traditional QoS metrics of delay, delay jitter, and packet loss probability (PLP), Quality of Experience (QoE) is now widely accepted as a numerical proxy for actual user experience. The literature has reported many mathematical mappings between QoE and QoS. These QoS parameters are measured by the network providers using sampling. There are some papers studying sampling errors in QoS measurements; however there is no account of propagation of these sampling errors to QoE evaluation. In this paper, we used industrially acquired measurements of PLP and jitter to evaluate the sampling errors and correlation in measurements. Focussing on Video-on-demand (VoD) applications, we use subjective testing and regression to map QoE metrics onto PLP and jitter. The resulting mathematical functions of QoE and theory of error propagation was used to evaluate the propagated error in QoE, and this error was represented as confidence interval. Using the guidelines of UK government for sampling, our results indicate that confidence intervals around estimated QoE in a busy hour can be between MOS=1 to MOS=5 at targeted operating point of QoS parameters. These results are a new perspective on QoE evaluation, and are of great significance to all organisations that need to estimate the QoE VoD applications precisely.
DATA DESCRIPTOR | doi:10.20944/preprints202109.0370.v1
Subject: Engineering, Energy & Fuel Technology Keywords: smart meter data; household survey; EPC; energy data; energy demand; energy consumption; longitudinal; energy modelling; electricity data; gas data
Online: 22 September 2021 (10:16:05 CEST)
The Smart Energy Research Lab (SERL) Observatory dataset described here comprises half-hourly and daily electricity and gas data, SERL survey data, Energy Performance Certificate (EPC) input data and 24 local hourly climate reanalysis variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) for over 13,000 households in Great Britain (GB). Participants were recruited in September 2019, September 2020 and January 2021 and their smart meter data are collected from up to one year prior to sign up. Data collection will continue until at least August 2022, and longer if funding allows. Survey data relating to the dwelling, appliances, household demographics and attitudes was collected at sign up. Data are linked at the household level and UK-based academic researchers can apply for access within a secure virtual environment for research projects in the public interest. This is a data descriptor paper describing how the data was collected, the variables available and the representativeness of the sample compared to national estimates. It is intended as a guide for researchers working with or considering using the SERL Observatory dataset, or simply looking to learn more about it.
ARTICLE | doi:10.20944/preprints202104.0332.v1
Subject: Engineering, Automotive Engineering Keywords: smart water grid; advanced metering infrastructure; short-term water demand forecasting; end-use level; on-site sodium hypochlorite generator
Online: 13 April 2021 (09:20:08 CEST)
It is crucial to forecast the water demand accurately for supplying water efficiently and stably in a water supply system. In particular, accurately forecasting short-term water demand helps in saving energy and reducing operating costs. With the introduction of the Smart Water Grid (SWG) in a water supply system, the amount of water consumption is obtained in real time through an advanced metering infrastructure (AMI) sensor, which can be used for forecasting the short-term water demand. The models widely used for water demand forecasting include the autoregressive integrated moving average, radial basis function-artificial neural network, quantitative multi-model predictor plus, and long short-term memory. However, there is a lack of research on assessing the performance of models and forecasting the short-term water demand by applying the data on the amount of water consumption by purpose and the pipe diameter of an end-use level of the SWG demonstration plant in each demand forecasting model. Therefore, in this study, the short-term water demand was forecasted for each model using the data collected from the AMI, and the performance of each model was assessed. The Smart Water Grid Research Group installed ultrasonic-wave-type AMI sensors in the block 112 located in YeongJong Island, Incheon, and the actual data used for operating the SWG demonstration plant were adopted. The performance of the model was assessed by using the residual, root mean square error (RMSE), normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency (NSE), and Pearson correlation coefficient (PCC) as indices. The water demand forecast was slightly underestimated in models that employed the assessment results based on the RMSE and NRMSE. Furthermore, the forecasting accuracy was low for the NSE due to a large number of negative values; the correlation between the observed and forecasted values of the PCC was not high, and it was difficult to forecast the peak amount of water consumption. Therefore, as the short-term water demand forecasting models using only time and the amount of water consumption have limitations in reflecting the characteristics of consumers, a water supply system can be managed more precisely if other factors (weather, customer behavior, etc.) influencing the water demand are applied.
ARTICLE | doi:10.20944/preprints202205.0103.v1
Subject: Engineering, Biomedical & Chemical Engineering Keywords: chemical oxygen demand (COD); zero liquid discharge (ZLED); poly-aluminum chloride; chemi-cal-coagulation; jar-test; Microbial Fuel Cell (MFC)
Online: 9 May 2022 (05:48:07 CEST)
This study develops into the application of a combined MFC unit with chemical coagulation for total treatment of inert contaminants in complex substrates. Microbial Fuel Cell (MFC) technology converts chemical energy in the form of organic matter, into bioelectricity in an environmentally friendly and effi-cient manner, reducing carbon emissions and increasing bioenergy production. An evaluation of a la-boratory scale chemical coagulation using an aluminum and poly-based coagulant on how effective it can remove bulk impurities such as particulate COD and turbidity to obtain the purest and most cost-effectively treated wastewater using a jar test is being conducted in this current study. This study aims to find the most effective treatment technologies for wastewater recovery in breweries in order to achieve zero liquid effluent discharge (ZLED). The preliminary results showed that adding a modest amount of poly and a 50 % alum alone treatment improved COD, color, and turbidity reduction. The turbidity removal efficiency achieved after chemical coagulation treatment was 90.50 % and 59.36 % COD removal, demonstrating the benefits of adopting an alum/poly based technique. To determine ZLED, this study clearly advised a combined treatment technique, specifically the MFC-flocculator unit for efficient organics and inorganics removal.
ARTICLE | doi:10.20944/preprints201903.0101.v1
Subject: Engineering, Civil Engineering Keywords: nutrient resources recovery , chemical oxygen demand (COD), carbon to nitrogen ratio (C/N), co-composting, wastewater sludge, municipal solid wastes (MSW)
Online: 8 March 2019 (04:06:02 CET)
The purpose of this study is nutrient resources recovery by achieving the optimal chemical oxygen demand (COD) and carbon to nitrogen ratio (C/N) in co-composting wastewater treatment plant sludge with Municipal Solid Wastes (MSW). In this effort, the co-composting has been conducted in form of a case study in the northern region of Iran. In this research, 192 tests were carried out on four series of samples examined in terms of waste to sludge ratio, different aeration period, the percent of porous materials and the moisture content. This study was carried out at a temperature of 50 °C for a 15 day period by application of the in-vessel system and shows that the best ratio for waste to sludge is 2:1, while the 8 hour period is the best aeration period. The porous material which can be added to the composting process is limited to 15% in weight. In other words, any more or less amount of this material will adversely impact the process. Moreover, this research suggests that the sludge dewatering is not required in such processes. In Addition, the efficiency of both COD and C/N reductions equals to about 40%.
REVIEW | doi:10.20944/preprints201812.0235.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: Intelligent Load Forecasting 1; Demand-Side Management 2; Pattern Similarity 3; Hierarchical Forecasting 4; Feature Selection 5; Weather Station Selection 6
Online: 19 December 2018 (12:19:14 CET)
Electricity demand forecasting has been a real challenge for power system scheduling in the different levels of the energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for load forecasting; although, scant evidence is available about the feasibility of each of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationale behind intelligent forecasting methods, based on the work of previous researchers in the field of energy. The fundamental benefits and main drawbacks of the aforementioned methods are discussed in order to depict the efficiency of each approach in various situations. In the end, a proposed hybrid strategy is represented.