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Traditional mid‐term electricity forecasting models rely on calendar and meteorological information such as temperature and wind speed to achieve high performance. However depending on such variables has drawbacks, as they may not be informative enough during extreme weather. While ubiquitous, textual sources of information are hardly included in prediction algorithms for time series, despite the relevant information they may contain. In this work, we propose to leverage openly accessible weather reports for electricity demand and meteorological time series prediction problems. Our experiments on French and British load data show that the considered textual sources allow to improve overall accuracy of the reference model, particularly during extreme weather events such as storms or abnormal temperatures. Additionally, we apply our approach to the problem of imputation of missing values in meteorological time series, and we show that our text‐based approach beats standard methods. Furthermore, the influence of words on the time series' predictions can be interpreted for the considered encoding schemes of the text, leading to a greater confidence in our results. [ABSTRACT FROM AUTHOR]

In recent years, the relationship between a country's gross domestic product (GDP) and its electricity consumption has changed significantly due to increased energy efficiency (EE) and renewable energy (RE) adoption. This decoupling disrupts conventional load forecasting models, affecting utility companies. This study has developed an innovative solution using an artificial neural network (ANN) Hybrid method for load forecasting, resulting in a remarkably accurate model with 99.68% precision. Applying this model to Malaysia's electricity consumption from 2020 to 2040 reveals a significant 13% reduction when accounting for EE and RE trends. This method aids risk management, contingency planning, and decision-making by accurately reflecting changing energy usage dynamics influenced by EE and RE sources. [ABSTRACT FROM AUTHOR]

This study re-examines the stationarity properties of per capita electricity consumption in 18 MENA countries from 1980–2021. We use a novel panel stationarity test with a Fourier approximation to capture structural breaks and nonlinearities in the data. This Fourier panel test accounts for cross-sectional dependence and allows heterogeneity across cross-sections in the panel. Moreover, we apply several new panel stationarity tests that complement the aforementioned Fourier test. The study finds strong empirical evidence supporting the stationarity of per capita electricity consumption in the MENA region when considering smooth structural breaks, and our results remain unchanged using sharp structural breaks panel stationarity test. Therefore, we conclude that policies to manage energy demand will have no long-run effect as per capita electricity consumption will, after a shock, return to its long-run trend path. [ABSTRACT FROM AUTHOR]

Ensuring effective forecasting of buildings' energy consumption is crucial in establishing a greater understanding and improvement of buildings' energy efficiency. In Singapore, domestic electricity usage in public residential buildings takes up a significant portion of the country's annual energy consumption. Having effective forecasting approaches is thus important in supporting relevant strategies and policy making. In this research, we proposed a hybrid approach that was based on a combination of building characteristics and urban landscape variables to predict residential housing electricity usage in Singapore. XGboost was also incorporated inside the hybrid approach as the preferred machine learning approach for energy consumption predictions. To demonstrate our proposed approach's predictive strength, the performance of our proposed hybrid machine learning approach was compared with two other models, Geographically Weighted Regression (GWR) model and the Random Forest (RF) model. Results showed that our proposed hybrid model had outperformed these abovementioned approaches with higher accuracy (r2 value of 0.9). The proposed approach had thus been effective in forecasting electricity consumption for public housing in Singapore, and it could also be utilised in other similar urban areas for future electricity consumption forecasting. [ABSTRACT FROM AUTHOR]

With the growth of the nations, the commercial and public services sectors have recently seen an increase in their electricity usage. This demonstrates how crucial it is to understand a building's behavior in order to lower its usage. This requires on-site data collection by qualified professionals and specialized equipment, which represents high costs. However, multiple studies have demonstrated that it is possible to find electricity-saving strategies from the study of electricity usage, recorded in an hourly period or less, captured by smart meters. In this context, the present study applies a methodology to determine useful information on the operation and characteristics of public buildings on the Ecuadorian coast based on the data gathered over a period of five consecutive months from smart meters. The methodology consists of four steps: (1) data cleaning and filling, (2) time-series decomposition, (3) the generation of consumption profile and (4) the identification of the temperature influence. According to the results, the pre-cooling of spaces accounts for 5% of all electricity used in the commercial buildings, while prolonged shutdown uses 10%. Approximately USD 1100 per month would be spent on the main building and USD 78 on the agency as a result. [ABSTRACT FROM AUTHOR]

AbstractThis paper examines the spatiotemporal inequality in electricity consumption at the subnational level of Bangladesh using nighttime light (NTL) data. The NTL data, sourced from the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) for the period from 2013 to 2020, reveals persistent variability in electricity consumption among the districts. Notably, the gap between urban and non-urban areas has widened. While within district inequality (measured by NTL Gini) has declined over time, it remains high in several districts. Convergence analysis confirms that while lagging districts are showing a catching up effect, the sub-districts are diverging among themselves (in terms of mean NTL per capita). Interestingly, the rural sub-districts are converging among themselves despite urban sub-district divergence. The study also identifies regions with significant imbalance between NTL, population, and built-up area density values. These findings have implications for policymakers aiming to ensure electricity for all and reduce inequality. [ABSTRACT FROM AUTHOR]

Technology Revolution 5.0, characterized by the integration of cutting-edge technologies (CET) like artificial intelligence (AI), internet of things (IoT), and blockchain into various facets of life, has brought remarkable advancements and conveniences. However, this era has also raised significant concerns regarding its environmental impact. The paper applies the ARDL (autoregressive distributed lag approach). The manuscript applied the World Bank data from 2000 to 2022. This paper aims to delve into the determinants contributing to carbon dioxide emissions in the context of industrial revolution 5.0, focusing on Singapore as a case study. The article combines a review of the existing literature, an analysis of the Singaporean environmental landscape, and empirical findings to shed light on this critical issue. The empirical study shows that electricity consumption and foreign direct investment significantly negatively affect environmental pollution in Singapore; fossil fuel and import positively influence ecological pollution. This article helps policymakers have policy implications for Singaporeans in the future. [ABSTRACT FROM AUTHOR]

This paper examines the nexus between carbon dioxide (CO2) emissions, electricity consumption, fossil fuels, foreign direct investment (FDI), gross domestic product (GDP), and renewable energy in the Philippines. This paper also explores the intricate relationships between carbon dioxide (CO2) emissions, electricity consumption, fossil fuel use, foreign direct investment (FDI), gross domestic product (GDP), and renewable energy in the Philippines. Utilizing time-series data from 1990 to 2022 and applying advanced econometric techniques such as vector error correction modeling (VECM) and Granger causality tests, the study reveals the significant impacts of economic growth and energy consumption on CO2 emissions. The findings highlight the crucial role of renewable energy in mitigating environmental degradation. Policy implications are discussed in the context of the Philippines' commitment to sustainable development and climate change mitigation, emphasizing the need for integrated policies that promote renewable energy and energy efficiency alongside economic growth. We use a comprehensive econometric analysis to understand these variables' dynamic interactions and causal relationships. The study employs time-series data from 1990 to 2022 and applies advanced econometric techniques, including vector error correction modeling (VECM) and Granger causality tests. The results highlight the significant impact of economic growth and energy consumption on CO2 emissions while also underscoring the critical role of renewable energy in mitigating environmental degradation. Policy implications are discussed considering the Philippines' commitment to sustainable development and climate change mitigation. [ABSTRACT FROM AUTHOR]

Accurately predicting industrial electricity consumption is essential for optimizing energy efficiency, and reducing costs in industrial operations. This study presents a novel hybrid prediction model based on radial basis function neural network (RBFNN) and kernelized support vector regression (KSVR) methods (RBFNN-KSVR) for estimating industrial electricity consumption. Key input variables include population, electricity price in the industry sector, gross domestic product (GDP), and the number of electricity subscribers. The proposed hybrid model was implemented in a real-world case study to estimate industrial electricity consumption in Iran and compared against base methods (RBFNN, SVR, and KSVR). Extensive evaluation reveals the superior performance of the RBFNN-KSVR model in predicting industrial electricity consumption. This study provides a robust and reliable approach for industrial stakeholders to enhance energy planning, identify energy-saving opportunities, and ensure a stable power supply. The findings have significant implications for optimizing energy usage, improving efficiency, and reducing costs in industrial operations. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) usually begins by grouping data according to various criteria, most often by days of the week. Then, based on the obtained segments, independent models are created. Each model's prediction uses only one segment of the data. This paper proposes a new approach to model formation based on the correlation between the forecasted day and previous days. The proposed approach is compared with the usual approach where data segments are obtained by grouping according to days of the week. The models were created using fuzzy logic and ANFIS. The mean absolute percentage errors of the new approach and the usual approach using ANFIS in terms of prediction accuracy are obtained as 2.89 and 4.15, respectively. The mean absolute percentage errors for the new approach and the usual approach are 3.39 and 4.78, respectively, when fuzzy logic is used. The results showed that when the proposed method is used, forecasts for the day ahead are much more accurate in both cases. [ABSTRACT FROM AUTHOR]

This study uses the Scopus and Web of Science databases to review quantitative methods to forecast electricity consumption from 2015 to 2024. Using the PRISMA approach, 175 relevant publications were identified from an initial set of 821 documents and subsequently subjected to bibliometric analysis. This analysis examined publication trends, citation metrics, and collaboration patterns across various countries and institutions. Over the period analyzed, the number of articles has steadily increased, with a more rapid rise observed after 2020. Although China dominates this research field, strong bibliographic coupling worldwide indicates significant international collaboration. The study suggests that no single method consistently outperforms others across all contexts and that forecasting methods should be adapted to regional contexts, considering specific economic, social, and environmental factors. Furthermore, we emphasize that review papers should compare methods and results regarding both time horizon and temporal resolution, as these aspects are crucial for the accuracy and applicability of the forecasts. [ABSTRACT FROM AUTHOR]

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Copyright of Arab Gulf Journal of Scientific Research is the property of Arabian Gulf University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This community service aims to promote energy efficiency awareness for the home industry sector, so that energy efficiency becomes a culture both in household domestic activities and in the production process. The method of implementing this service uses training with the participants of this activity are the residents around Keranggan Ecotourism Village who run businesses and have household industries. The evaluation instrument uses in this community service is the increase of awareness level of the participants regarding energy efficiency behaviour, such as selecting energy efficient equipment, recording the electricity consumption, and setting the operational hour of energy-using equipments such as lamps, rice cookers, and so on. The results of this service show that participants gained knowledge in recording electricity consumption and knowing the activities that have an impact on increasing electricity consumption and develop plans for energy efficiency programs.

Effective short-term load forecasting (STLF) is essential for optimizing electricity grid operations. This study focuses on refining STLF for day-ahead predictions using Bayesian multiple linear regression (BMLR). This study’s originality lies in its innovative use of BMLR combined with data clustering techniques to improve prediction accuracy, a method not previously explored in existing literature. We address the critical issue of input data clustering, highlighting its impact on prediction accuracy. Four clustering methods based on temporality were examined, with clustering by weekday and hour proving most effective for BMLR-based STLF. Predictors included historical load, temperature, season, weekday, and hour, selected using the Akaike information criterion (AIC). Linear regression assumptions were verified, and solutions were proposed for deviations, notably addressing heteroscedasticity. Autocorrelation in residuals was addressed to improve forecasting efficiency. Time-cross validation and performance metrics demonstrated model effectiveness. Second-degree polynomial terms are included for better fitting. Clustering by weekday and hour is optimal for BMLR-based STLF, aiding accurate load forecasts. The main objectives of this research are to determine the optimal clustering method for BMLR in STLF and to provide practical insights into the application of Bayesian techniques in load forecasting. This research significantly contributes to the field of STLF by providing practical insights into data clustering and model refinement, offering valuable perspectives for enhanced energy management and grid stability. [ABSTRACT FROM AUTHOR]

This research investigates the complex relationship between households' electricity consumption and a range of environmental factors, such as air pollution and meteorological parameters. Data regarding air pollutants PM 10 [μg/m^3 ], and NO2 [μg/m^3 ], as well as meteorological parameters temperature [°C] elative humidity [%], and atmospheric pressure [hPa] were collected from the Federal Hydrometeorological Institute of Bosnia and Herzegovina, while data related to electricity consumption were obtained from the Public Enterprise Elektroprivreda BiH d.d. - Sarajevo. Measurements and data collection were done for a period of time from January 2020 until March 2022 allowing for seasonal variations to be taken into account. The research findings reveal strong negative correlation of households' electricity consumption with temperature, moderate positive correlation with PM10 and NO2 as well as weak positive correlation with relative humidity and very weak positive correlation with pressure. Linear regression model developed in this research showed that temperature, humidity and concentration of air pollutant PM10 were significant variables and had impact on households' electricity consumption. The results of this research provide valuable insights into the complex dynamics between households' electricity consumption and environmental factors, and can help policymakers, electricity providers as well as households to develop better ways to save energy and develop better electricity consumption strategies.

Introduction: Modeling energy demand in different energy consuming sectors is a crucial measure for effective management of the energy sector and appropriate policies to increase productivity. The rising importance of energy resources in economic development is evident. Sustainable energy use is crucial for environmental protection and social progress. Understanding the factors affecting energy consumption is essential for effective energy management. Therefore, the purpose of the current study is to investigate the impact of environmental factors on household electricity consumption in Yazd city. Materials and Methods: In the present research, various environmental factors affecting electricity consumption, including air pollution, air temperature in homes, ground surface temperature, and green space were investigated. The effects of these factors on electricity consumption of subscribers were investigated with ANN and apriori methods. Results: Among the environmental factors, the distance to the regional park, the area of the park, and the amount of vegetation at a distance of 300m have the greatest impact, respectively, and the average summer air temperature, the amount of vegetation at a radius of 500 m, the distance from the local park, and the average summer NDVI have had the smallest effect. Unlike neural network methods, apriori presents relationships between parameters affecting electricity consumption transparently in the form of rules. Conclusion: It's used to identify the most frequently occurring elements and meaningful associations in a dataset. Greenspace can be a mitigation strateegy for reduction of energy consumption.

Electricity expense management presents significant challenges, as this resource is susceptible to various influencing factors. In universities, the demand for this resource is rapidly growing with institutional expansion and has a significant environmental impact. In this study, the machine learning models long short-term memory (LSTM), random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost) were trained with historical consumption data from the Federal Institute of Paraná (IFPR) over the last seven years and climatic variables to forecast electricity consumption 12 months ahead. Datasets from two campuses were adopted. To improve model performance, feature selection was performed using Shapley additive explanations (SHAP), and hyperparameter optimization was carried out using genetic algorithm (GA) and particle swarm optimization (PSO). The results indicate that the proposed cooperative ensemble learning approach named Weaker Separator Booster (WSB) exhibited the best performance for datasets. Specifically, it achieved an sMAPE of 13.90% and MAE of 1990.87 kWh for the IFPR–Palmas Campus and an sMAPE of 18.72% and MAE of 465.02 kWh for the Coronel Vivida Campus. The SHAP analysis revealed distinct feature importance patterns across the two IFPR campuses. A commonality that emerged was the strong influence of lagged time-series values and a minimal influence of climatic variables.

The electricity consumption in the smart grids consists of an uncertainty feature. Also, an unstable atmosphere situation causes photovoltaic (PV) generation will be undefined output. With both of these problems, the net demand power of consumers can’t be a specific value. On the other hand, using consumption patterns, the consumptions and generations could be predicted for improving the operation of the power system. This paper reports the results of the differential performance of probabilistic forecasting of the residential electricity onsumption, PV power generation, and net demand related to smart buildings using the novel method of the Improved Deep Mixture Density Network (IDMDN). According to this, investigators used a strong Multi to Multi (M2M) mapping of the neural network model. They followed that they had used a kind of beta kernel to decrease the number of leakage issues. It is an attempt to generate random predictions by the method of end-to-end. It expressed a new performance of changed initiation and multiple procedures of educating to decrease or remove unsteady traits in the probabilistic problems of the beta kernel function. The results show good performance of the proposed method in comparison with other methods.

As the global average temperature has increased due to climate change, the use of air conditioning equipment for cooling homes has become more popular. Inverter equipment is advertised as a better energy option than systems with an on/off control; however, there is a lack of sufficient studies to prove this. This work aims to analyze and compare the electricity consumption associated with cooling equipment with an on/off control and inverter equipment. A heat transfer model coupled with energy balance for a room is developed and implemented in Python 3.12. The indoor temperature is controlled by simulating an on/off control and a PID control for the inverter system. Subsequently, the electricity consumption of the two systems is compared, and a sensitivity analysis is performed to determine which variables have the greatest impact on electricity consumption. The results show that the inverter equipment has lower electricity consumption compared to the equipment with the on/off control. However, the sensitivity analysis shows that the indoor temperature set point plays a more relevant role since a 15% variation in its value impacts electricity consumption by up to 77%. [ABSTRACT FROM AUTHOR]

Electricity is fundamental to modern societies and will become even more so as its use expands through different technologies and population growth. Power generation is currently the largest source of carbon-dioxide (CO2) emissions globally, but it is also the sector that is leading the transition to net zero emissions through the rapid rise of renewables. The impacts of COVID-19 on the electricity sector led to a reduction in the demand for electricity, while at the same time, the current global energy crisis has placed the security and affordability of electricity at the top of the political agenda in many countries. In this way, the decrease in the demand for electricity, as well as its gradual recovery, makes it necessary to carry out energy planning that considers the adverse effects caused by global events with a high socioeconomic impact. In this article, the Low Emission Analysis Platform (LEAP) 2020 software has been used to determine the distribution of energy sources to 2050 for Mexico. The variables that lead to the possible profiles for 2050 are social, economic, and technological. The results correspond to a possible future based on official data from the National Electric System (SEN) of Mexico. The forecast for 2050 indicates that the electricity sector will have almost double the current installed capacity; however, emissions do not correspond to twice as much: they are practically 50% higher. [ABSTRACT FROM AUTHOR]

Although industrial kitchens consume significantly more energy than other commercial buildings and represent an important opportunity for sustainable energy systems, researchers have largely overlooked energy efficiency in these spaces. One of the main challenges is the diversity of kitchen configurations, complicating the characterization and generalization of research findings, including establishing a standardized methodology for assessing and benchmarking energy demand. To address this research gap, this paper proposes a methodological framework to develop taxonomies for understanding the electricity consumption in industrial kitchens. The proposed framework was developed following an extensive survey of the existing literature, and it is based on four main steps: identification of the knowledge domain, extraction of terms and concepts, data collection, and information analysis. To demonstrate the proposed framework, a case study was developed involving the participation of 50 restaurants located in Portugal. The proposed framework proved valid as it enabled the construction of a taxonomy that allows the classification of industrial kitchens according to different energy consumption-related concepts, such as costs with energy, the physical size of the kitchen, and the number of workers. [ABSTRACT FROM AUTHOR]

Electricity expense management presents significant challenges, as this resource is susceptible to various influencing factors. In universities, the demand for this resource is rapidly growing with institutional expansion and has a significant environmental impact. In this study, the machine learning models long short-term memory (LSTM), random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost) were trained with historical consumption data from the Federal Institute of Paraná (IFPR) over the last seven years and climatic variables to forecast electricity consumption 12 months ahead. Datasets from two campuses were adopted. To improve model performance, feature selection was performed using Shapley additive explanations (SHAP), and hyperparameter optimization was carried out using genetic algorithm (GA) and particle swarm optimization (PSO). The results indicate that the proposed cooperative ensemble learning approach named Weaker Separator Booster (WSB) exhibited the best performance for datasets. Specifically, it achieved an sMAPE of 13.90% and MAE of 1990.87 kWh for the IFPR–Palmas Campus and an sMAPE of 18.72% and MAE of 465.02 kWh for the Coronel Vivida Campus. The SHAP analysis revealed distinct feature importance patterns across the two IFPR campuses. A commonality that emerged was the strong influence of lagged time-series values and a minimal influence of climatic variables. [ABSTRACT FROM AUTHOR]

Research conducted around the world shows that energy harvesting (EH) systems can be used in contemporary vehicles powered by combustion engines, hybrid or electric motors. Unfortunately, the efficiency of modern combustion engines is only about 40%, the remaining energy is lost and can be recovered to some extent. Therefore, the search is ongoing for systems that will use this part of the energy to power specific systems or micro-sensors installed in the vehicle. The article presents the possibilities of energy harvesting from four main sources in the vehicle: energy during braking, energy from the damping of the vehicle suspension, from the exhaust system and energy from the vibrations of the combustion engine. Based on the analysis of the literature on the presented research of various scientific centres and the author's experiment, it can be concluded that there is a huge potential for obtaining thermal energy from the engine exhaust system and the vehicle suspension system. A field that has not been explored much, but according to the authors also has energy potential, is the recovery of energy generated during vibrations in the suspension of an internal combustion engine in the engine compartment of the vehicle. For the obtained measurement data from the experiments, initial digital processing of the signal was performed using a low-pass filter, then the fast fourier transform (FFT) and the Hilbert-Huang Transformation (HHT) were used. Preliminary research shows the possibility of mounting the energy recovery system in the engine compartment and the potential possibility of obtaining electricity in certain operating states of the combustion engine. [ABSTRACT FROM AUTHOR]

Studying user electricity consumption behavior is crucial for understanding their power usage patterns. However, the traditional clustering methods fail to identify emerging types of electricity consumption behavior. To address this issue, this paper introduces a statistical analysis of clusters and evaluates the set of indicators for power usage patterns. The fuzzy C-means clustering algorithm is then used to analyze 6 months of electricity consumption data in 2017 from energy storage equipment, agricultural drainage irrigation, port shore power, and electric vehicles. Finally, the proposed method is validated through experiments, where the Davies-Bouldin index and profile coefficient are calculated and compared. Experiments showed that the optimal number of clusters is 4. This study demonstrates the potential of using a fuzzy C-means clustering algorithm in identifying emerging types of electricity consumption behavior, which can help power system operators and policymakers to make informed decisions and improve energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we study the predictive power of electricity consumption data for regional economic activity. Using unique high‐frequency electricity consumption data from industrial firms for the second‐largest German state, the Free State of Bavaria, we conduct a pseudo out‐of‐sample forecasting experiment for the monthly growth rate of Bavarian industrial production. We find that electricity consumption is the best performing indicator in the nowcasting setup and has higher accuracy than other conventional indicators in a monthly forecasting experiment. Exploiting the high‐frequency nature of the data, we find that the weekly electricity consumption indicator also provides good predictions about industrial activity in the current month with only 2 weeks of information. Overall, our results indicate that regional electricity consumption is a promising avenue for measuring and forecasting regional economic activity. [ABSTRACT FROM AUTHOR]

The MGM (1, n) model has the characteristics of less data required, simple modeling, and high prediction accuracy. It has been successfully applied to short-term forecasting across various economic, social, and technological domains, yielding promising outcomes. There is insufficient attention paid to the interpolation coefficient of the model. The interpolation coefficients determine the extent of model fitting, which, in turn, impacts its prediction accuracy. This study made some improvements to the interpolation coefficients and proposed an improved MGM (1, n) model. IMGM (1, n) model and MGM (1, n) model were employed to compare the performance of the improved MGM (1, n) model. Upon a series of comparisons and analyses, it was concluded that the improved MGM (1, n) model has higher fitting and prediction accuracy than the other two forecasting methods. The method was used to forecast the short-term electricity consumption of Linfen City. The findings revealed that by 2030, the electricity demand in Linfen City is projected to be 563.7 billion kWh. [ABSTRACT FROM AUTHOR]

In the West African Monetary and Economic Union (UEMOA), information on the characteristics of the users and patterns of electricity end-uses remains hard to find. This study aims to contribute to reducing the gap in research on domestic electricity consumption in the region by unveiling the ownership rates, patterns of use and electricity consumption of domestic appliances in urban households through a city-wide survey. Three categories of urban users were investigated including high, medium and low consumers. Findings demonstrated various ownership rates for appliances, ranging from 100% for lighting fixtures to 0% for washing machines depending on user category. Domestic electricity demonstrated patterns consisting of three peak demand periods, with the main ones occurring in the evening (19:00 to 20:00) and the night (22:00). Other demand characteristics include an average daily electricity use ranging from 0.50 to 6.42 kWh per household, a maximum power demand of between 0.19 and 0.70 kW and a daily load factor between 35 and 58%. Finally, the appliances contributing the most to domestic electricity use include air-conditioners, fans, fridges and freezers, televisions and lighting fixtures, with contributions differing from one category of user to another. Policy implications including review of the appliances' importations framework and policies, and incentives for purchasing efficient appliances, design of more tailored policies, considering the different backgrounds of the users, education enhancement on energy behaviours for increasing energy efficiency/conservation, and implementation of DSM programs including load levelling, load shifting and load reducing depending on the type of appliance for energy conservation in the domestic buildings were derived. Overall, a large range of stakeholders of the electricity sector, not only in the West African Economic and Monetary Union (UEMOA), but also in other regions and countries sharing common characteristics should be interested in the results of this study. [ABSTRACT FROM AUTHOR]

This study investigated the impact of meteorological factors on electricity consumption in arid regions, characterized by extreme temperatures and high humidity. Statistical approaches such as multiple linear regression (MLR) and multiplicative time series (MTS), alongside the advanced machine learning method Extreme Gradient Boosting (XGBoost) were utilized to analyze historical consumption data. The models developed were rigorously evaluated using established measures such as the Coefficient of Determination ( R 2 ), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The performance of the models was highly accurate, with regression-type models consistently achieving an R 2 greater than 0.9. Additionally, other metrics such as RMSE and MAPE demonstrated exceptionally low values relative to the overall data scale, reinforcing the models' precision and reliability. The analysis not only highlights the significant meteorological drivers of electricity consumption but also assesses the models' effectiveness in managing seasonal and irregular variations. These findings offer crucial insights for improving energy management and promoting sustainability in similar climatic regions. [ABSTRACT FROM AUTHOR]

The main objective of the current study is to analyze the nexus between public debt, renewable electricity, economic growth, and carbon dioxide (CO2) emissions in emerging economies between 1990 and 2020. The augmented mean group (AMG), fully modified ordinary least square (FMOLS), and dynamic ordinary least square (DOLS) models have been applied to analyze the long-run estimation. The empirical evidence demonstrates that public debt, renewable electricity reduces CO2 emissions. Furthermore, an N-shaped relationship has been identified between per capita CO2 emissions and per capita GDP in emerging nations. Also, the result reveals a bidirectional causal relationship between public debt and economic growth, CO2 emissions and economic growth, public debt and CO2 emissions, and renewable electricity and economic growth. The current study recommends promoting the renewable energy transition, elevating renewable electricity generation capacities, and ensuring greener economic growth by emitting carbon dioxide emissions across emerging countries. The government across each region could incorporate taxes and other incentives to encourage entrepreneurs and citizens to produce equipment that reduces carbon intensity and is ecologically friendly. [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]

This research investigates the complex relationship between households' electricity consumption and a range of environmental factors, such as air pollution and meteorological parameters. Data regarding air pollutants [ ] and [ ], as well as meteorological parameters temperature [°C], relative humidity [ ], and atmospheric pressure [ ] were collected from the Federal Hydrometeorological Institute of Bosnia and Herzegovina, while data related to electricity consumption were obtained from the Public Enterprise Elektroprivreda BiH d.d. - Sarajevo. Measurements and data collection were done for a period of time from January 2020 until March 2022 allowing for seasonal variations to be taken into account. The research findings reveal strong negative correlation of households' electricity consumption with temperature, moderate positive correlation with, and, as well as weak positive correlation with relative humidity and very weak positive correlation with pressure. Linear regression model developed in this research showed that temperature, humidity and concentration of air pollutant were significant variables and had impact on households' electricity consumption. The results of this research provide valuable insights into the complex dynamics between households' electricity consumption and environmental factors, and can help policymakers, electricity providers as well as households to develop better ways to save energy and develop better electricity consumption strategies. [ABSTRACT FROM AUTHOR]

The start-up is a transient state of operation of a compression-ignition engine during which many negative phenomena occur that affect the technical condition of the engine and its electrical devices and the surroundings. The start-up process of an compression-ignition engine is influenced by many factors, such as: technical condition of the starting system, technical condition of the engine, battery charge level, lubricant properties, engine standstill time, temperature of the engine, etc. Mechanical energy is required to start the engine, supplied by an electric starter by drives the engine's crankshaft. The paper presents the results of experimental tests of electrical parameters of the single-cylinder diesel engine start-up process at variable fuel injection parameters under ambient temperature conditions. Higher values of the measured electrical parameters (Imax, Umin, Pmax, Pmed) were obtained for the nominal FD1 fuel dose, compared to the tests with an increased FD2 fuel dose. Knowledge about the operating parameters of the electric starter during the start-up process is important for the engine user (vehicle driver) and of course for designers of modern combustion engine starting systems as well as service personnel. [ABSTRACT FROM AUTHOR]

Integrating network expansion planning into electric vehicle (EV) smart charging solutions involves designing scalable infrastructure to accommodate the growing demand for electric mobility while considering grid capacity and energy distribution efficiency. This paper proposes a hybrid approach for EV smart charging with network expansion planning. The hybrid technique is the joint execution of the coati optimization algorithm (COA) and cascade‐correlation‐growing deep learning neural network, commonly known as the COA‐CCG‐DLNN technique. The objective of the proposed method is to minimize the cost of charging EVs, and it forecasts the best course of action. EV charging with network expansion is based on vehicle‐to‐building (V2B), vehicle‐to‐grid (V2G), and grid‐to‐vehicle (G2V). The COA approach is used to minimize the cost of EV charging and the CCG‐DLNN approach is used to predict the optimal solution for the system. The proposed method is executed on the MATLAB platform and is compared with existing techniques like particle swarm optimization (PSO), heap‐based optimization (HBO), and wild horse optimization (WHO). The proposed method achieves a low cost of $1.33 and a high accuracy of 99.5% compared with other existing techniques. The performance metrics for the proposed method include 2996.348 as the best result, 3000.100 as the mean, 3001.261 as the worst, and a standard deviation of 1.160348, along with a median of 2998.816, all of which outperform other existing methods. [ABSTRACT FROM AUTHOR]

The prediction of electricity consumption plays a vital role in promoting sustainable development, ensuring energy security and resilience, facilitating regional planning, and integrating renewable energy sources. A novel electricity consumption characterization and prediction model based on land use was proposed. This model achieves land-use subdivision to provide highly correlated variables; exhibits strong interpretability, thereby revealing even marginal effects of land use on electricity consumption; and demonstrates high performance, thereby enabling large-scale simulations and predictions. Using 297 cities and 2,505 counties as case studies, the key findings are as follows: (1) The model demonstrates strong generalization ability (R2 = 0.91), high precision (Kappa = 0.77), and robustness, with an overall prediction accuracy exceeding 80 %; (2) The marginal impact of industrial land on electricity consumption is more complex, with more efficiency achieved by limiting its area to either 104.3 km2 or between 288.2 and 657.3 km2; (3) The marginal impact of commercial and residential land on electricity consumption exhibits a strong linear relationship (R2 > 0.80). Restricting the scale to 11.3 km2 could effectively mitigate this impact. Mixed commercial and residential land is advantageous for overall electricity consumption control, but after exceeding 43.5 km2, separate layout considerations for urban residential land are necessary; (4) In 2030, Shanghai's electricity consumption is projected to reach 155,143 million kW·h, making it the highest among the 297 cities. Meanwhile, Suzhou Industrial Park leads among the 2,505 districts with a consumption of 30,996 million kW·h; (5) Identify future electricity consumption hotspots and clustering characteristics, evaluate the renewable energy potential in these hotspot areas, and propose targeted strategies accordingly.

This study explores the nexus between electricity consumption, fossil fuel use, renewable energy adoption, population growth, GDP, and environmental pollution in Indonesia, incorporating open innovation dynamics as a novel dimension of analysis. The study uncovers significant long-term relationships between the variables using time series data from 2000 to 2023 and applying econometric techniques such as cointegration analysis, Granger causality tests, and Vector Error Correction Models (VECM). The results indicate that electricity consumption drives economic growth and CO₂ emissions, while fossil fuel dependency significantly contributes to environmental pollution. Renewable energy adoption, though growing, is insufficient to offset the environmental damage caused by fossil fuel use. The study also identifies the potential of renewable energy to mitigate pollution levels, though challenges such as technological barriers and high initial investment costs remain. Population growth intensifies energy demand and pollution, underscoring the necessity for sustainable energy policies. Additionally, the research highlights the role of open innovation, particularly in renewable energy, mobile payment platforms, and collective intelligence, in addressing Indonesia's energy-environment challenges. Policy recommendations emphasize the need for more substantial incentives for renewable energy, the regulation of fossil fuel consumption, and the development of an open innovation ecosystem that engages both the public and private sectors.