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Over time, several relationships have been defined between electricity consumption and a region's social and economic variables, with income as the main factor. This paper uses multiple correspondence analysis to identify the categories of dwellings and, from a graphical point of view (positioning maps), the effects of the different characteristics that influence the electricity consumption of households in rural areas of Cundinamarca, Colombia. In this analysis, the consumption of residential users responded mainly to what they can afford or acquire based on their income, consumption habits, and the characteristics of the technology. Furthermore, this study highlights the implications of these findings for policymakers and energy providers, providing valuable insights for developing targeted strategies to promote energy efficiency and sustainability in rural areas. This research contributes to a deeper understanding of the dynamics of electricity consumption and highlights the importance of tailoring energy-related interventions to the specific socio-economic context of rural communities, in this case in Cundinamarca. [ABSTRACT FROM AUTHOR]

Global energy consumption is growing rapidly, with the frequency and intensity of extreme events constantly increasing, posing a long-term threat to power supply and consumption. Therefore, analyzing the spatiotemporal characteristics of electricity consumption and quantitatively assessing the impact of extreme events on electricity consumption are of great significance. Based on fine-grained electricity consumption data from Europe for the years 2019–2022, this paper employs a data mining perspective and four methods including Z-score, Isolation Forest, Local Outlier Factor, and Autoencoder to detect abnormal electricity consumption during extreme events. Additionally, it combines indicators such as elastic loss, vulnerability, and duration to measure the impact of extreme events on electricity consumption. It is found that low temperatures could lead to abrupt changes in electricity consumption, with Northern Europe being more significantly affected by low temperatures. The COVID-19 pandemic had the most significant impact on electricity consumption in Europe, with the middle part of Europe being the hardest hit during the first wave of the pandemic. Electricity anomalies during the pandemic period were related to national pandemic control policies and exhibited some lag. High temperatures persisted for a longer duration in the middle part of Europe. [ABSTRACT FROM AUTHOR]

This paper addresses electricity consumption management in manufacturing enterprises. The research aims to provide manufacturing enterprises with an effective tool to control electricity costs. Recently, some factors have been observed to affect the rapid changes in the operating conditions of enterprises. These include the transformation of the power sector toward renewable energy, the disruption of supply chains resulting from a coronavirus pandemic, political crises, and process automation. A method for the analysis and management of electricity consumption in enterprises based on simulation modeling is proposed. The simulation model contains predefined objects representing physical system elements and the data processing algorithm. The production order execution time, energy consumption, employee overtime, and machine load are included in the model. The results show that it is possible to determine the level of power available for the process completion and its influence on the production volume and realization time. In the studied case, when the available power was reduced by half, there was an increase in order execution time of nearly 25 percent and an increase in energy consumption of nearly 15 percent. The method can be used in the operational activities of enterprises as well as extended to different types of production processes. [ABSTRACT FROM AUTHOR]

Over the past three decades, China's economy has witnessed remarkable growth, with an average annual growth rate over 9%. However, China also faces great challenges to balance this spectacular economic growth and continuously increasing energy use like many other economies in the world. With the aim of designing effective energy and environmental policies, policymakers are required to master the relationship between energy consumption and economic growth. Therefore, in the case of North China, a multivariate model employing panel data analysis method based on the Cobb-Douglas production function which introduces electricity consumption as a main factor was established in this paper. The equilibrium relationship and causal relationship between real GDP, electricity consumption, total investment in fixed assets, and the employment were explored using data during the period of 1995-2014 for six provinces in North China, including Beijing City, Tianjin City, Hebei Province, Shanxi Province, Shandong Province and Inner Mongolia. The results of panel co-integration tests clearly state that all variables are co-integrated in the long term. Finally, Granger causality tests were used to examine the causal relationship between economic growth, electricity consumption, labor force and capital. From the Granger causality test results, we can draw the conclusions that: (1) There exist bi-directional causal relationships between electricity consumption and real GDP in six provinces except Hebei; and (2) there is a bi-directional relationship between capital input and economic growth and between labor force input and economic growth except Beijing and Hebei. Therefore, the ways to solve the contradiction of economic growth and energy consumption in North China are to reduce fossil energy consumption, develop renewable and sustainable energy sources, improve energy efficiency, and increase the proportion of the third industry, especially the sectors which hold the characteristics of low energy consumption and high value-added. [ABSTRACT FROM AUTHOR]

The use of fossil fuel-based energy systems that provide heat and electricity to a building has adverse environmental impacts. These impacts can be mitigated, to a certain extent, through the incorporation of renewable energy sources (RES). The primary objective of this study was to conduct an environmental assessment of the performance of energy systems in existing public facilities located in Poland. Based on the findings, we proposed and implemented changes to these systems and validated the environmental impact of the RES systems used. SimaPro 8.1 software and the Ecoinvent 3.0 database were employed for the analysis, which entailed an environmental assessment of six public facilities located in Poland. The installation of RES resulted in an average 27% reduction in electricity consumption from the national electricity grid. This reduction was observed to be the least in the hospital and the most in the religious building. This was reflected in the environmental assessment of heating systems. The implementation of RES reduced the environmental impact of the religious building by an average of 20%. Concurrently, the CO2 emissions decreased by 35%, SO2 by 44%, and PM10 by 42%. Significant investments and the installation of advanced RES will not prevent the occurrence of unintentional environmental consequences unless the demand for electricity and thermal energy is reduced. The use of RES in the analyzed buildings and the associated avoided emissions do not entirely offset the negative emissions resulting from the utilization of other (conventional) energy sources in the analyzed energy systems of public buildings. Consequently, the analyzed facilities collectively exert a detrimental impact on the environment. [ABSTRACT FROM AUTHOR]

Thanks to various European directives, individuals are empowered to share and trade electricity within Renewable Energy Communities, enhancing the operational efficiency of local energy systems. The digital transformation of the energy market enables the integration of decentralized energy resources using cloud computing, the Internet of Things, and artificial intelligence. In order to assess the feasibility of new business models based on data-driven solutions, various electricity consumption time series are necessary at this level of aggregation. Since these are currently not yet available in sufficient quality and quantity, and due to data privacy reasons, synthetic time series are essential in the strategic planning of smart grid energy systems. By enabling the simulation of diverse scenarios, they facilitate the integration of new technologies and the development of effective demand response strategies. Moreover, they provide valuable data for assessing novel load forecasting methodologies that are essential to manage energy efficiently and to ensure grid stability. Therefore, this research proposes a methodology to synthesize electricity consumption time series by applying the Box–Jenkins method, an intelligent sampling technique for data augmentation and a probabilistic forecast model. This novel approach emulates the stochastic nature of electricity consumption time series and synthesizes realistic ones of Renewable Energy Communities concerning seasonal as well as short-term variations and stochasticity. Comparing autocorrelations, distributions of values, and principle components of daily sequences between real and synthetic time series, the results exhibit nearly identical characteristics to the original data and, thus, are usable in designing and studying efficient smart grid systems. [ABSTRACT FROM AUTHOR]

Electricity is widely used around 80% of the world. Electricity theft has dangerous effects on utilities in terms of power efficiency and costs billions of dollars per annum. The enhancement of the traditional grids gave rise to smart grids that enable one to resolve the dilemma of electricity theft detection (ETD) using an extensive amount of data formulated by smart meters. This data are used by power utilities to examine the consumption behaviors of consumers and to decide whether the consumer is an electricity thief or benign. However, the traditional data-driven methods for ETD have poor detection performances due to the high-dimensional imbalanced data and their limited ETD capability. In this paper, we present a new class balancing mechanism based on the interquartile minority oversampling technique and a combined ETD model to overcome the shortcomings of conventional approaches. The combined ETD model is composed of long short-term memory (LSTM), UNet and adaptive boosting (Adaboost), and termed LSTM–UNet–Adaboost. In this regard, LSTM–UNet–Adaboost combines the advantages of deep learning (LSTM-UNet) along with ensemble learning (Adaboost) for ETD. Moreover, the performance of the proposed LSTM–UNet–Adaboost scheme was simulated and evaluated over the real-time smart meter dataset given by the State Grid Corporation of China. The simulations were conducted using the most appropriate performance indicators, such as area under the curve, precision, recall and F1 measure. The proposed solution obtained the highest results as compared to the existing benchmark schemes in terms of selected performance measures. More specifically, it achieved the detection rate of 0.92, which was the highest among existing benchmark schemes, such as logistic regression, support vector machine and random under-sampling boosting technique. Therefore, the simulation outcomes validate that the proposed LSTM–UNet–Adaboost model surpasses other traditional methods in terms of ETD and is more acceptable for real-time practices. [ABSTRACT FROM AUTHOR]

Electricity is an essential resource that plays a vital role in modern society, and its demand has increased rapidly alongside industrialization. The accurate forecasting of a country's electricity demand is crucial for economic development. A high-precision electricity forecasting framework can assist electricity system managers in predicting future demand and production more accurately, thereby effectively planning and scheduling electricity resources and improving the operational efficiency and reliability of the electricity system. To address this issue, this study proposed a hybrid forecasting framework called T-LGBKS, which incorporates TPE-LightGBM, k-nearest neighbor (KNN), and the Shapley additive explanation (SHAP) methods. The T-LGBKS framework was tested using Chinese provincial panel data from 2005 to 2021 and compared with seven other mainstream machine learning models. Our testing demonstrated that the proposed framework outperforms other models, with the highest accuracy ( R 2 = 0.9732 ). This study also analyzed the interpretability of this framework by introducing the SHAP method to reveal the relationship between municipal electricity consumption and socioeconomic characteristics (such as how changes in economic strength, traffic levels, and energy structure affect urban electricity demand). The findings of this study provide guidance for policymakers and assist decision makers in designing and implementing electricity management systems in China. [ABSTRACT FROM AUTHOR]

The introduction of smart meters and time-use survey data is helping decision makers to understand the residential electricity consumption behaviour behind load profiles. However, it can be difficult to obtain the actual detailed consumption data due to privacy issues. Synthesising residential electricity consumption profiles may be an alternative way to develop synthetic load profiles that initially starts by reviewing the existing synthetic load profile methods. The purpose of this review is to identify the recent methods for synthesising residential electricity load profiles by conducting a rigorous standalone literature review. This review study has been applied and presented transparently and is replicable by other researchers. The review has answered the following research questions: the definition, concept and roles of residential electricity load profile and synthesised data; recent approaches and methods; research purposes; applicable simulations and validation methods of the final selected studies. The results show that the most applied approach in modelling residential electricity load profiles is the bottom-up approach. As it is detailed, it suitable to reflect the local residential behaviour in electricity consumption. Consequently, it is more complex to develop and calibrate the model as identified in the results. Bottom-up models are more powerful in analysing energy consumptions that focus on behavioural patterns, dwelling profiles and control strategies. [ABSTRACT FROM AUTHOR]

This paper analyzes electricity markets in Slovenia during the specific period of market deregulation and price liberalization. The drivers of electricity prices and electricity consumption are investigated. The Slovenian electricity markets are analyzed in relation with the European Energy Exchange (EEX) market. Associations between electricity prices on the one hand, and primary energy prices, variation in air temperature, daily maximum electricity power, and cross-border grid prices on the other hand, are analyzed separately for industrial and household consumers. Monthly data are used in a regression analysis during the period of Slovenia's electricity market deregulation and price liberalization. Empirical results show that electricity prices achieved in the EEX market were significantly associated with primary energy prices. In Slovenia, the prices for daily maximum electricity power were significantly associated with electricity prices achieved on the EEX market. The increases in electricity prices for households, however, cannot be explained with developments in electricity prices on the EEX market. As the period analyzed is the stage of market deregulation and price liberalization, this can have important policy implications for the countries that still have regulated and monopolized electricity markets. Opening the electricity markets is expected to increase competition and reduce pressures for electricity price increases. However, the experiences and lessons learned among the countries following market deregulation and price liberalization are mixed. For industry, electricity prices affect cost competitiveness, while for households, electricity prices, through expenses, affect their welfare. A competitive and efficient electricity market should balance between suppliers' and consumers' market interests. With greening the energy markets and the development of the CO2 emission trading market, it is also important to encourage use of renewable energy sources. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic obliged the Romanian government to take drastic measures to contain the virus. More than this, they imposed the heaviest restrictions in the EU. For more than a month, during the lockdown period, everything stopped: schools and universities had only online classes, national and international flights and gatherings were forbidden, and many restrictions for travel were imposed. This paper analyzes the changes that occurred in electricity consumption linked with economic growth, during the pandemic, in Romania. For a better understanding of the correlations between gross domestic product (GDP) and electricity consumption (EC) in different economic contexts, the period 2008–2020 was divided into three series: the 2008–2012 financial crisis and the post-crisis recovery period, the 2013–2019 period of economic growth, and the Q1–Q3 2020 pandemic period. Using correlation coefficients and regression analysis, the authors found that the GDP decoupled from EC in the first period. The increase in GDP led to an increase in the consumption of electricity and the electricity produced from RESs in the second period. In Q3 2020, the real GDP is different from the calculated GDP, due to the pandemic. In Romania, the electricity consumption decreased within the first nine months of the pandemic due to the economic contraction. The electricity that comes from coal and hydropower plants suffered the biggest decrease. If the electricity that comes from NRESs can be adapted to the economic demands, the quantity of electricity that comes from RESs will be influenced by the climate conditions. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of the COVID-19 pandemic on economic growth and electricity consumption and investigates the hypothesis of the influence of this consumption on the gross domestic product (GDP) for Romania. Using time series on monthly electricity consumption and quarterly GDP and a multi-linear regression model, we performed an analysis of the evolution of these indicators for 2007–2020, a comparison between their behavior during the financial crisis vs. COVID-19 crisis, and empirically explore the relationships between GDP and electricity consumption or some of its components. The results of the analysis confirm that the shock of declining activity due to the COVID-19 pandemic had a severe negative impact on electric energy consumption and GDP in the first half of 2020, followed by a slight recovery. By using a linear regression model, long-term relationships between GDP and domestic and non-household electricity consumptions were found. The empirically estimated elasticity coefficients confirm the more important impact of non-household electricity consumption on GDP compared to the one of domestic electricity consumption. In the context of the COVID-19 pandemic, the results of the study could be useful for optimizing energy and economic growth policies at the national and European levels. [ABSTRACT FROM AUTHOR]

The present study investigates electricity consumption, carbon dioxide (CO2) emission, and economic growth decoupling using data from 1971 to 2020 for the economy of China. The study uses decoupling analysis (DA) as the prime methodology for analysis. Furthermore, the findings put forward a significant contribution to an economic picture of the economy of China and a sizeable addition to related research and findings under the assigned issues discussed in the study. The study's main contribution is to decouple electricity consumption from the gross domestic product (GDP), which is rare in the existing literature in the context of China. Moreover, the study shows the decoupling of environment affects electricity consumption, and GDP growth. The DA model shows that electricity consumption is the main driving force enhancing economic growth. However, industrialization has increased greenhouse gases, global warming, and climate change due to production and consumption. China's economy uses coal for energy resources, which indicates that China produces a large proportion of electricity with coal, which causes high CO2 emissions. Finally, further analysis with the Granger causality test confirms the main findings. [ABSTRACT FROM AUTHOR]

In today's modern world, monthly forecasts of electricity consumption are vital in planning the generation and distribution of energy utilities. However, the properties of these time series are so complex that they are difficult to model directly. Thus, this study provides a comprehensive analysis of forecasting monthly electricity consumption by comparing several decomposition techniques followed by various time series models. To this end, first, we decompose the electricity consumption time series into three new subseries: the long-term trend series, the seasonal series, and the stochastic series, using the three different proposed decomposition methods. Second, to forecast each subseries with various popular time series models, all their possible combinations are considered. Finally, the forecast results of each subseries are summed up to obtain the final forecast results. The proposed modeling and forecasting framework is applied to data on Pakistan's monthly electricity consumption from January 1990 to June 2020. The one-month-ahead out-of-sample forecast results (descriptive, statistical test, and graphical analysis) for the considered data suggest that the proposed methodology gives a highly accurate and efficient gain. It is also shown that the proposed decomposition methods outperform the benchmark ones and increase the performance of final model forecasts. In addition, the final forecasting models produce the lowest mean error, performing significantly better than those reported in the literature. Finally, we believe that the framework proposed for modeling and forecasting can also be used to solve other forecasting problems in the real world that have similar features. [ABSTRACT FROM AUTHOR]

This study aims to develop statistical and machine learning methodologies for forecasting yearly electricity consumption in Saudi Arabia. The novelty of this study include (i) determining significant features that have a considerable influence on electricity consumption, (ii) utilizing a Bayesian optimization algorithm (BOA) to enhance the model's hyperparameters, (iii) hybridizing the BOA with the machine learning algorithms, viz., support vector regression (SVR) and nonlinear autoregressive networks with exogenous inputs (NARX), for modeling individually the long-term electricity consumption, (iv) comparing their performances with the widely used classical time-series algorithm autoregressive integrated moving average with exogenous inputs (ARIMAX) with regard to the accuracy, computational efficiency, and generalizability, and (v) forecasting future yearly electricity consumption and validation. The population, gross domestic product (GDP), imports, and refined oil products were observed to be significant with the total yearly electricity consumption in Saudi Arabia. The coefficient of determination R 2 values for all the developed models are >0.98, indicating an excellent fit of the models with historical data. However, among all three proposed models, the BOA–NARX has the best performance, improving the forecasting accuracy (root mean square error (RMSE)) by 71% and 80% compared to the ARIMAX and BOA–SVR models, respectively. The overall results of this study confirm the higher accuracy and reliability of the proposed methods in total electricity consumption forecasting that can be used by power system operators to more accurately forecast electricity consumption to ensure the sustainability of electric energy. This study can also provide significant guidance and helpful insights for researchers to enhance their understanding of crucial research, emerging trends, and new developments in future energy studies. [ABSTRACT FROM AUTHOR]

Society's concerns with electricity consumption have motivated researchers to improve on the way that energy consumption management is done. The reduction of energy consumption and the optimization of energy management are, therefore, two major aspects to be considered. Additionally, load forecast provides relevant information with the support of historical data allowing an enhanced energy management, allowing energy costs reduction. In this paper, the proposed consumption forecast methodology uses an Artificial Neural Network (ANN) and incremental learning to increase the forecast accuracy. The ANN is retrained daily, providing an updated forecasting model. The case study uses 16 months of data, split in 5-min periods, from a real industrial facility. The advantages of using the proposed method are illustrated with the numerical results. [ABSTRACT FROM AUTHOR]

The building sector consumes as much as 80% of generated electricity in the UAE; during the COVID-19 pandemic, the energy consumption of two sub-sectors, i.e., commercial (50%) and residential (30%), was significantly impacted. The residential sector was impacted the most due to an increase in the average occupancy during the lockdown period. This increment continued even after the lockdown due to the fear of infection. The COVID-19 pandemic and its lockdown measures can be considered experimental setups, allowing for a better understanding of how users shift their consumption under new conditions. The emergency health measures and new social dynamics shaped the residential sector's energy behavior and its increase in electricity consumption. This article presents and analyzes the identified issues concerning residential electricity consumers and how their behaviors change based on the electricity consumption data during the COVID-19 period. The Dubai Electricity and Water Authority conducted a voluntary survey to define the profiles of its residential customers. A sample of 439 consumers participated in this survey and four years of smart meter records. The analysis focused on understanding behavioral changes in consumers during the COVID-19 period. At this time, the dwellings were occupied for longer than usual, increasing their domestic energy consumption and altering the daily peak hours for the comparable period before, during, and after the lockdown. This work addressed COVID-19 and the lockdown as an atypical case. The authors used a machine learning model and the consumption data for 2018 to predict the consumption for each year afterward, observing the COVID-19 years (2020 and 2021), and compared them with the so-called typical 2019 predictions. Four years of fifteen-minute resolution data and the detailed profiles of the customers led to a better understanding of the impacts of COVID-19 on residential energy use, irrespective of changes caused by seasonal variations. The findings include the reasons for the changes in consumption and the effects of the pandemic. There was a 12% increase in the annual consumption for the sample residents considered in 2020 (the COVID-19-affected year) as compared to 2019, and the total consumption remained similar with only a 0.2% decrease in 2021. The article also reports that machine learning models created in only one year, 2018, performed better by 10% in prediction compared with the deep learning models due to the limited training data available. The article implies the need for exploring approaches/features that could model the previously unseen COVID-19-like scenarios to improve the performance in case of such an event in the future. [ABSTRACT FROM AUTHOR]

Reliable energy consumption forecasting is essential for building energy efficiency improvement. Regression models are simple and effective for data analysis, but their practical applications are limited by the low prediction accuracy under ever-changing building operation conditions. To address this challenge, a Joinpoint–Multiple Linear Regression (JP–MLR) model is proposed in this study, based on the investigation of the daily electricity usage data of 8 apartment complexes located within a university in Xiamen, China. The univariate model is first built using the Joinpoint Regression (JPR) method, and then the remaining residuals are evaluated using the Multiple Linear Regression (MLR) method. The model contains six explanatory variables, three of which are continuous (mean outdoor air temperature, mean relative humidity, and temperature amplitude) and three of which are categorical (gender, holiday index, and sunny day index). The performance of the JP–MLR model is compared to that of the other four data-driven algorithm models: JPR, MLR, Back Propagation (BP) neural network, and Random Forest (RF). The JP–MLR model, which has an R2 value of 95.77%, has superior prediction performance when compared to the traditional regression-based JPR model and MLR model. It also performs better than the machine learning-based BP model and is identical to that of the RF model. This demonstrates that the JP–MLR model has satisfactory prediction performance and offers building operators an effective prediction tool. The proposed research method also provides also serves as a reference for electricity consumption analysis in other types of buildings. [ABSTRACT FROM AUTHOR]

With the global increase in demand for energy, energy conservation of research and development buildings has become of primary importance for building owners. Knowledge based on the patterns in energy consumption of previous years could be used to predict the near-future energy usage of buildings, to optimize and facilitate more effective energy consumption. Hence, this research aimed to develop a generic model for predicting energy consumption. Air-conditioning was used to exemplify the generic model for electricity consumption, as it is the process that often consumes the most energy in a public building. The purpose of this paper is to present this model and the related findings. After causative factors were determined, the methods of linear regression and various machine learning techniques—including the earlier machine learning techniques of support vector machine, random forest, and multilayer perceptron, and the later machine learning techniques of deep neural network, recurrent neural network, long short-term memory, and gated recurrent unit—were applied for prediction. Among them, the prediction of random forest resulted in an R2 of 88% ahead of the first month and 81% ahead of the third month. These experimental results demonstrate that the prediction model is reliable and significantly accurate. Building owners could further enrich the model for energy conservation and management. [ABSTRACT FROM AUTHOR]

The integration of photovoltaic (PV) and electric vehicle (EV) charging in residential buildings has increased in recent years. At high latitudes, both pose new challenges to the residential power systems due to the negative correlation between household load and PV power production and the increase in household peak load by EV charging. EV smart charging schemes can be an option to overcome these challenges. This paper presents a distributed and a centralized EV smart charging scheme for residential buildings based on installed photovoltaic (PV) power output and household electricity consumption. The proposed smart charging schemes are designed to determine the optimal EV charging schedules with the objective to minimize the net load variability or to flatten the net load profile. Minimizing the net load variability implies both increasing the PV self-consumption and reducing the peak loads. The charging scheduling problems are formulated and solved with quadratic programming approaches. The departure and arrival time and the distance covered by vehicles in each trip are specifically modeled based on available statistical data from the Swedish travel survey. The schemes are applied on simulated typical Swedish detached houses without electric heating. Results show that both improved PV self-consumption and peak load reduction are achieved. The aggregation of distributed smart charging in multiple households is conducted, and the results are compared to the smart charging for a single household. On the community level, both results from distributed and centralized charging approaches are compared. [ABSTRACT FROM AUTHOR]

In Poland, electricity production is based on nonrenewable energy sources. The energy market is dominated by corporations. Rising electricity prices and threats of power shortages have become, among others, prerequisites for the development of distributed sources based on renewable sources, primarily using photovoltaic panels. The development of the renewable energy market is slow due to the lack of legal solutions and insufficient state support. Polish farmers, especially fruit growers, whose electricity consumption costs are a significant item in production costs, may become part of the energy sector by building a local energy system. In 2022, Poland introduced two new legal regulations, which currently provide three options for obtaining electricity using renewable micro-installations. The conducted study aimed at indicating which of the legally permitted option is the most advantageous for fruit farms in Poland. Growers can produce energy by being a prosumer who settles accounts in the net-metering and net-billing system and by participating in an energy cooperative. The direction of changes was ascertained by conducting a pilot study among fruit growers. The study used real quantitative and valuable data on the consumption and costs of electricity in 2012–2022 and the production of photovoltaic panels in a fruit farm in 2021. The study consisted of comparing three models of settling accounts for electricity with two comparative models in two scenarios. The analysis of prosumers' functioning showed that the situation in Poland is favorable for the development of civic energy. However, participation in an energy cooperative with new legal solutions will not always be the more advantageous solution in terms of economy and energy. In particular, the study showed that the support mechanism needs further changes. The results of the research confirm the existing potential of fruit farms, which can be developed in the area of securing energy needs. The results of this research can be used as the premise for introducing changes to the legal conditions for the establishment and operation of energy cooperatives in Poland. [ABSTRACT FROM AUTHOR]

In order to fulfill the European Energy Performance of Buildings Directive (EPBD) requirements regarding the reduction of energy consumption in buildings, great attention is paid to primary energy consumption. Wind energy is considered a type of primary energy. The analysis of the literature has revealed that wind energy is evaluated by different methods. Therefore, the aim of this article is to calculate the effect of the parameters of wind sources and wind speed on the primary energy factor of wind turbines. In order to achieve this aim, the primary energy factor of investigated 100 wind turbines and 11 wind farms operating in Lithuania was calculated. The results of the investigation show that the difference in the non-renewable primary energy factors between wind turbines with regard to their capacity is 35%. In addition, primary energy factor (PEF) values depend on geographic location and climate conditions. This paper provides a recommendation that the EU energy efficiency and renewable energy directives and regulations of all EU member states should use the same or, at least, a very similar methodology for the calculation of the primary energy factors of renewable and non-renewable energy sources. [ABSTRACT FROM AUTHOR]

It has been established that a consistent supply of electricity to sectors of an economy is vital for economic growth. Countries in West Africa have not realized their full economic potential due to limited access to electricity. The problem with limited energy consumption has been compounded by an unstable supply of power. In this regard, diverse studies have sought to ascertain the factors that influence the consumption of electricity. However, in West Africa, there are very limited empirical works carried out to establish the determinants of electricity consumption and this has necessitated this study. The pooled OLS method is used to examine the determinant consumption of electricity for the period 1980 to 2018. In addition, the study focuses on four countries in West Africa: Nigeria, Ghana, Cote d'Ivoire and Senegal. The results indicate that gross domestic product, foreign direct investment, trade openness, industry output and population growth show a positive and significant relationship with electricity consumption. However, consumer price index has a negative and significant influence on the quantum of electricity consumed. The study recommends that projects focused to increase electricity generation capacity in West Africa should be encouraged and energy from clean sources should be harnessed to provide electricity. [ABSTRACT FROM AUTHOR]

In Poland, in 2021, an increase in demand for electricity was recorded, and hard coal and lignite power plants still had a dominant share in its production. Another source of electricity was renewable energy sources (RES), mainly wind farms. Young people in Poland are aware that electricity is not only its production, but also consumption in households. Therefore, it is also essential to properly educate young people, aiming at a cost-effective, sustainable lifestyle, in relation to electricity consumption. The article presents the current state of the electricity generation sector in Poland along with the proposed changes in this respect, in particular in terms of the development prospects for the use of renewable energy sources and the influence of government administration on the production and consumption of electricity. The aim of this research was to broaden the knowledge of young people's opinions on energy production and consumption. The research results can be used to create long-term directions of energy policy and to build a social attitude of sustainable energy consumption in Poland. The research was non-probabilistic, based on questionnaires, using the CAWI (Computer Assisted Web Interview) technique. The questionnaire was conducted in 2021, and the analysis was made on the basis of 741 correctly completed research questionnaires. The results of the research confirmed the research hypotheses—that the surveyed youth see the need to reduce consumption as a way to counteract climate change and excessive energy consumption. They also expect government support in the energy transformation in Poland, based on a diversified scenario, using both renewable energy sources (RES) and nuclear energy. [ABSTRACT FROM AUTHOR]

Understanding the relationship between electricity consumption and economic growth is crucial for formulating efficient energy policies for all sectors in general, and for agriculture in particular. To study this, an empirical examination of the long-run co-movement and the causal relationship between electricity consumption and real gross state domestic product (GSDP) from agriculture and its allied sectors is attempted. The agricultural sector involves the use of different input technologies that are further influenced by electricity consumption. To account for this technology-enabling effect of electricity, we further take up an analysis of the relationship between electricity consumption and agricultural technology factors: fertilizer consumption, the share of irrigation, area under cereal, and the extent of mechanization. We use both state (data from 17 states for the period 1993–2017 for the electricity–GSDP relationship) and country-level data (for the period 1980–2018 for the electricity–technology factors relationship) in the analysis. Since short-period time series datasets analysis may yield unreliable and inconsistent results, we employed modern heterogeneous panel co-integration and panel-based error correction model techniques for analyzing the energy–growth linkage. When the heterogeneous states effect is taken into account, the empirical results fully support a positive long-run co-integrated connection between GSDP and electricity consumption. We could detect both long-run and short-run unidirectional causality running from electricity consumption to agricultural growth. Further electricity consumption was also found to augment the use of technology factors in agriculture. This calls for implementing policies and strategies for achieving higher electricity use in agriculture and improved efficiency in its utilization simultaneously. [ABSTRACT FROM AUTHOR]

Current power grids in many countries are not fully prepared for high electric vehicle (EV) penetration, and there is evidence that the construction of additional grid capacity is constantly outpaced by EV diffusion. If this situation continues, then it will compromise grid reliability and cause problems such as system overload, voltage and frequency fluctuations, and power losses. This is especially true for densely populated areas where the grid capacity is already strained with existing old infrastructure. The objective of this research is to identify the zip-code level electricity consumption that is associated with large-scale EV adoption in New York City, one of the most densely populated areas in the United States (U.S.). We fuse the Fisher and Pry diffusion model and Rogers model within the agent-based simulation to forecast zip-code level EV diffusion and the required energy capacity to satisfy the charging demand. The research outcomes will assist policy makers and grid operators in making better planning decisions on the locations and timing of investments during the transition to smarter grids and greener transportation. [ABSTRACT FROM AUTHOR]

Recent literature has shown that electricity demand elasticities may not be constant over time and this has investigated using time-varying estimation methods. As accurate modeling of electricity demand is very important in Azerbaijan, which is a transitional country facing significant change in its economic outlook, we analyze whether the response of electricity demand to income and price is varying over time in this economy. We employed the Time-Varying Coefficient cointegration approach, a cutting-edge time-varying estimation method. We find evidence that income elasticity demonstrates sizeable variation for the period of investigation ranging from 0.48% to 0.56%. The study has some useful policy implications related to the income and price aspects of the electricity consumption in Azerbaijan. [ABSTRACT FROM AUTHOR]

In recent years, energy conservation research has identified a number of household actions that have the potential to drive significant reductions in carbon emissions in the near-term, without requiring substantial changes to householders' lifestyles or imposing significant financial costs. In this qualitative study, we investigate the potential of some of these actions for behavioral modification by asking householders to reveal the reasons why they perform (or fail to perform) such actions. As part of a telephone survey, a sample of customers (n = 1541) from an Australian energy retailer were asked about their reasons for engaging in specific energy usage practices in one of five household domains: laundry, kitchen, bathroom, space heating/cooling or general appliance usage. Qualitative analyses of participants' open-ended responses revealed that practices in the laundry and kitchen appear to hold the greatest promise for behavioral change, whereas practices in the shower may be more challenging to modify. Integrating our findings with current psychological and sociological knowledge, we present a range of possibilities for future behavior change interventions at the practice-level. [ABSTRACT FROM AUTHOR]

Improving the estimation accuracy for the energy consumption of electric vehicles (EVs) would greatly contribute to alleviating the range anxiety of drivers and serve as a critical basis for the planning, operation, and management of charging infrastructures. To address the challenges in energy consumption estimation encountered due to sparse Global Positioning System (GPS) observations, an estimation model is proposed that considers both the kinetic characteristics from sparse GPS observations and the unique attributes of EVs: (1) work opposing the rolling resistance; (2) aerodynamic friction losses; (3) energy consumption/generation depending on the grade of the route; (4) auxiliary load consumption; and (5) additional energy losses arising from the unstable power output of the electric motor. Two quantities, the average energy consumption per kilometer and the energy consumption for an entire trip, were focused on and compared for model fitness, parameter, and effectiveness, and the latter showed a higher fitness. Based on sparse GPS observations of 68 EVs in Aichi Prefecture, Japan, the traditional linear regression approach and a multilevel mixed-effects linear regression approach were used for model calibration. The proposed model showed a high accuracy and demonstrated a great potential for application in using sparse GPS observations to predict the energy consumption of EVs. [ABSTRACT FROM AUTHOR]

Electricity consumption accounts for a considerable part of the final energy consumption, and it is important for economic development and human life. This study explores the spatiotemporal evolution characteristics and influencing factors of electricity consumption in the Yangtze River Delta region in China from 2006 to 2019, using the gravity model and Logarithmic Mean Divisia Index method, respectively. The results show that: (1) The centers of gravity for the total final, industrial and residential electricity consumptions have a trend of migration towards the west. (2) The distance of migration of the center of gravity for residential electricity consumption is the highest, and the trend of migration of the center of gravity for industrial and total final electricity consumptions are synchronous. (3) Economic development is the main reason for the growth in regional electricity consumption, and the decrease in the investment electricity consumption intensity inhibits the growth of electricity consumption. This study provides references to restrain the excessive increase in electricity consumption and improve the layout of power facilities at the regional level. [ABSTRACT FROM AUTHOR]

This work uses the outcome of a computational tool that performs Energy Performance Certification (EPC) data processing and transforms raw data into comparable data. Multi-correlation among variables results in probability distributions for the most relevant form and fabric building parameters. The model consistently predicts the distributions for heating and cooling energy needs for the Lisbon Metropolitan Area, with an error below 7% for the first, second and third quartiles. Differences in the energy needs estimation are below 6% when comparing the seasonal steady-state with the resistance-capacitance (RC) model, which proved to be a robust alternative algorithm capable of modeling hourly user profiles. The RC model calculates electricity consumption for actual, adequate, and minimum thermal comfort scenarios corresponding to different user profiles. The actual scenario, built from statistics and a previous survey, defines a reference to evaluate other scenarios for the mean electricity consumption for space heating and cooling in the building units with those systems. The results show that the actual mean electricity consumption for heating (610 kWh/y) is slightly above the minimum (512 kWh/y), with 37% of building units potentially under heated. The electricity consumption (108 kWh/y) for cooling is below the minimum (129 kWh/y). [ABSTRACT FROM AUTHOR]

The purpose of this study is to shed light on the nexus between electricity supply and economic growth in South Asian countries during 1990–2018. The study employs Pedroni's panel cointegration test as well as Dumitrescu and Hurlin's (DH) causality test for panel data. The empirical results confirm a long-term relationship between electricity supply and economic growth. We fail to reject the non-causal relationship between electricity supply and economic growth for the panel, thereby affirming the neutrality hypothesis. Single country causality analysis reveals the growth hypothesis in the case of Pakistan. These results have a number of policy implications. For example, governments can introduce measures to improve energy efficiency in Bangladesh, India and Sri Lanka without fear of harming economic growth. The results for Pakistan may also imply that fostering green energy generation would lead to a positive effect on economic growth via improved electricity production. The government may use various policy tools to stimulate adoption of renewable energy, such as fiscal incentives, low interest loans, or grants for rural populations to speed up the green energy transformation. [ABSTRACT FROM AUTHOR]

Countries with limited natural resources and high energy prices, such as Jordan, face significant challenges concerning energy consumption and energy efficiency, particularly in the context of climate change. Residential buildings are the most energy-consuming sector in Jordan. Photovoltaic (PV) systems on the rooftops of residential buildings can solve the problem of increasing electricity demands and address the need for more sustainable energy systems. This study calculated the potential electricity production from PV systems installed on the available rooftops of residential buildings and compared this production with current and future electricity consumption for residential households. A simulation tool using PV*SOL 2021 was used to estimate electricity production and a comparative method was used to compare electricity production and consumption. The results indicated that electricity production from PV systems installed on single houses and villas can cover, depending on the tilt angle and location of the properties, three to eight times their estimated future and current electricity use. PV installation on apartment buildings can cover 0.65 to 1.3 times their future and current electricity use. The surplus electricity produced can be used to mitigate urban energy demands and achieve energy sustainability. [ABSTRACT FROM AUTHOR]

Recently, the Member States of the European Union (EU) have found themselves in a controversial situation. On the one hand, national economic development is barely possible without increasing electricity consumption, whereas on the other we are facing increased use of natural resources (coal, oil, gas, wood), thermal effects, pollution and risks to human health. The European Green Deal is a response to the currently observed negative trends. The strategy aims to accelerate the economic development of the EU Member States, thus reducing electricity consumption. Objectives may include both the national economy and the electricity generation sector by applying advanced technologies and introducing innovations that increase output efficiency while reducing electricity costs. Assessing the current situation is vital for the successful implementation of the European Green Deal, i.e., by comparing the impact of electricity consumption on the economic development of the Member States. Thus, combining indicators for national economic development and the extent of electricity consumption into a single aggregate is necessary because electricity greatly affects economic development. The proposed methodology allows dividing the analysed EU Member States into three groups, in line with the degree of national economic development and the scope of electricity consumption in their economy sectors. [ABSTRACT FROM AUTHOR]

The use of air source heat pump systems for space heating and cooling is a convenient retrofitting strategy for reducing building energy costs. This can be combined with the rooftop installation of photovoltaic panels, which can cover, to a significant degree—or even significantly exceed the building's electricity needs, moving towards the zero energy building concept. Alternatively, increased capacity for rooftop photovoltaic (PV) installation may support the ongoing process of transforming the Greek power system away from the reliance on fossil fuels to potentially become one of the leaders of the energy transition in Europe by 2030. Standard building energy simulation tools allow good assessment of the Heating, Ventilation and Air Conditioning (HVAC) and PV systems' interactions in transient operation. Further, their use enables the rational sizing and selection of the type of panels type for the rooftop PV installation to maximize the return on investment. The annual performance of a three-zone residential building in Volos, Greece, with an air-to-water heat pump HVAC system and a rooftop PV installation, are simulated in a TRNSYS environment. The simulation results are employed to assess the expected building energy performance with a high performance, inverter driven heat pump with scroll compressor and high efficiency rooftop PV panels. Further, the objective functions are developed for the optimization of the installed PV panels' area and tilt angle, based on alternative electricity pricing and subsidies. The methodology presented can be adapted to optimize system design parameters for variable electricity tariffs and improve net metering policies. [ABSTRACT FROM AUTHOR]

The aim of this research was to identify energy saving instructions effect on household's electricity consumption. The research was conducted using Randomized Control Trials, which implied defining a treatment and control group on a sample of 330 households. The research was carried out in Republic of Serbia, where electricity prices are the lowest in Europe and electricity is used inefficiently. For quantitative analysis of data, the Difference in Difference method was used, which compares the changes in electricity consumption over time between the treatment and control group and estimates the overall impact of the energy saving instructions. The research showed that in situations where electricity price is very low, energy saving information does not have the significant impact on change in consumer behavior. However, inefficient use of electricity might be due to the different efficiency of heating devices used. Not only that the low impact of information on energy saving habits may be a consequence of the low will to change habit, but also of the impossibility to change the habit (unless changing the heating device, but this implies expenditures). Results can be used for consideration of changes in organization and regulation of the electricity market in all South Eastern European countries (SEE). [ABSTRACT FROM AUTHOR]

Between 60% and 70% of the total energy load of a house or office occurs through the exteriors of the building, and in the case of offices, heat loss from windows and doors can approach 40%. A need for glass that can artificially control the transmittance of visible light has therefore emerged. Smart windows with suspended particle device (SPD) film can reduce energy consumption by responding to environmental conditions. To measure the effect of SPD windows on the energy requirements for cooling and heating in Korea, we installed a testbed with SPD windows. With TRNSYS18, the comparison between measurements and simulation has been made in order to validate the simulation model with respect to the modeling of an SPD window. Furthermore, the energy requirements of conventional and SPD-applied windows were compared and analyzed for a standard building that represented an actual office building. When weather for the city of Anseong and a two-speed heat pump were used to verify the simulation, the simulated electricity consumption error compared with the testbed was −1.0% for cooling and −0.9% for heating. The annual electricity consumption error was −0.9%. When TMY2 Seoul weather data were applied to the reference building, the decrease in electricity consumption for cooling in the SPD model compared with the non-SPD model was 29.1% and the increase for heating was 15.8%. Annual electricity consumption decreased by 4.1%. [ABSTRACT FROM AUTHOR]

At the moment of writing, in Italy, there is an ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Its outbreak is leading to severe global socioeconomic disruptions impacting on all economic sectors from tourism, industry and the tertiary sector, up to the operational and opening of public offices, the closure of schools and the organization of families. Measures adopted by the Italian government to deal with the COVID-19 emergency have had direct effects both on people's daily lives and on the activity of most industrial and commercial production companies. These changes have been unequivocally reflected also on the Italian electricity system, which has shown unprecedented behavior in terms of both energy consumption and volume—and subsequently, in the observed share of renewable and conventional production technologies. The goal of this study is to show the impact on the power industry of all the restrictions and lockdown of the activities in Italy and to discuss the effects of COVID-19 outbreak on the bulk power system and the entire electricity sector. In particular, the consequences on load profiles, electricity consumption and market prices in Italy, including the environmental aspects, are examined. [ABSTRACT FROM AUTHOR]

We introduce the problem of load nowcasting to the energy forecasting literature. The recent load of the objective area is predicted based on limited available metering data within this area. Thus, slightly different from load forecasting, we are predicting the recent past using limited available metering data from the supply side of the system. Next, to an industry benchmark model, we introduce multiple high-dimensional models for providing more accurate predictions. They evaluate metered interconnector and generation unit data of different types like wind and solar power, storages, and nuclear and fossil power plants. Additionally, we augment the model by seasonal and autoregressive components to improve the nowcasting performance. We consider multiple estimation techniques based on the lassoand ridge and study the impact of the choice of the training/calibration period. The methodology is applied to a European TSO dataset from 2014 to 2019. The overall results show that in comparison to the industry benchmark, an accuracy improvement in terms of MAE and RMSE of about 60% is achieved. The best model is based on the ridge estimator and uses a specific non-standard shrinkage target. Due to the linear model structure, we can easily interpret the model output. [ABSTRACT FROM AUTHOR]

This article examines the asymmetric relationship between electric consumption, economic growth, and carbon dioxide emission in 15 countries over the period 1971–2014. We employed a nonlinear auto-regressive distribution Lag (NARDL) model approach to investigate the asymmetric cointegration between variables. Additionally, we applied the asymmetric causality approach to determine the causal relationship between variables. Results confirm nonlinear cointegration between variables in Cameroon, Congo Republic, Zambia, Canada, and the UK. The Wald test results confirm a long-run asymmetric link between electricity consumption, economic growth, and carbon emission in Canada and Cameroon, while a short-run asymmetric effect in the Congo Republic and the UK. Findings from the granger causality test are volatile across variables. The result provides strong support for the symmetric relationship between electric consumption, economic growth, and carbon emission in the short and long run. This study provides new evidence for policymakers to formulate country-specific policies to obtain better environmental quality while achieving sustainable economic growth. [ABSTRACT FROM AUTHOR]

Although the latest energy-efficient buildings use a large number of sensors and measuring instruments to predict consumption more accurately, it is generally not possible to identify which data are the most valuable or key for analysis among the tens of thousands of data points. This study selected the electric energy as a subset of total building energy consumption because it accounts for more than 65% of the total building energy consumption, and identified the variables that contribute to electric energy use. However, this study aimed to confirm data from a building using clustering in machine learning, instead of a calculation method from engineering simulation, to examine the variables that were identified and determine whether these variables had a strong correlation with energy consumption. Three different methods confirmed that the major variables related to electric energy consumption were significant. This research has significance because it was able to identify the factors in electric energy, accounting for more than half of the total building energy consumption, that had a major effect on energy consumption and revealed that these key variables alone, not the default values of many different items in simulation analysis, can ensure the reliable prediction of energy consumption. [ABSTRACT FROM AUTHOR]

Using remotely-sensed Suomi National Polar-orbiting Partnership (NPP)-VIIRS (Visible Infrared Imagery Radiometer Suite) night-time light (NTL) imagery between 2012 and 2016 and electricity consumption data from the IEA World Energy Balance database, we assemble a five-year panel dataset to evaluate if and to what extent NTL data are able to capture interannual changes in electricity consumption within different countries worldwide. We analyze the strength of the relationship both across World Bank income categories and between regional clusters, and we evaluate the heterogeneity of the link for different sectors of consumption. Our results show that interannual variation in nighttime light radiance is an effective proxy for predicting within-country changes in power consumption across all sectors, but only in lower-middle income countries. The result is robust to different econometric specifications. We discuss the key reasons behind this finding. The regions of Sub-Saharan Africa, Middle-East and North Africa, Latin America and the Caribbeans, and East Asia and the Pacific render a significant outcome, while changes in Europe, North America and South Asia are not successfully predicted by NTL. The designed methodological steps to process the raw data and the findings of the analysis improve the design and application of predictive models for electricity consumption based on NTL at different spatio-temporal scales. [ABSTRACT FROM AUTHOR]

To study Semi-Transparent photovoltaic (STPV) windows, experiments were conducted to test the energy potential of STPV window installed in buildings. Two identical rooms were built up as experimental units; one was fitted with amorphous silicon (a-silicon) photovoltaic (PV) windows, and another was fitted with a conventional window. The interactional influence was analyzed among air conditioning energy consumption, lighting energy consumption, and energy generation. It can be concluded that STPV windows could provide 0.26 kWh/per day and save 29% on comprehensive building load on a typical sunny day. In order to further investigate, buildings installed with STPV windows in four typical cities with different climate environments in southwest China were simulated and analyzed. The cooling load of the buildings were all decreased while the heating energy consumption and lighting energy consumption were lightly increased. The energy generation of STPV windows was highest in Lhasa at 402.1 kWh/year. The energy saving potential of STPV windows was predicted with good values; 54% in Kunming. [ABSTRACT FROM AUTHOR]