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Purpose: Electrical energy is in this day and age fundamental to the functioning of every household. The use of gas, heating oil or solid fuels for home heating also involves the supply of electricity. Today, electric power is the most environmentally friendly source of energy. Experience has shown that substitution of an oil heating system with a heat pump resulted in significant financial savings for the household. Design/methodology/approach: The purpose of this paper shall entail the construction of a multi-equation econometric model describing the mechanisms of electricity consumption in a specified household. The model takes the nature of a system of interdependent equations. Described shall be the monthly volume of electricity consumption, payment amount of for this energy (in PLN), and price per 1 MWh in a given month. Monthly time series from September 2015 to November 2022 have been used, which resulted in a time series with a count of 87 statistical observations. Findings: The econometric model of household electricity consumption presented in this paper confirms both the hypothesis about feedback between the variables USAGE and PRICE as well as the recursive effect of electricity consumption volume on its value in monetary units. In addition to the cognitive value of the econometric modeling results obtained, the empirical tool constructed makes enables forecast estimation of the energy consumption volume, its value and unit price in subsequent months, for at least 12 consecutive months. Practical implications: This type of research has great practical utility. They make it easier for rational interaction between electricity sellers and specific consumers. Social implications: Knowledge about the mechanisms of electricity consumption on the farm home may influence the rationalization of consumption and spending. This type of rationalization should have a positive impact on the environment natural, contributing to the reduction of greenhouse gas emissions. Originality/value: The novelty and originality of the work are the identification of the mechanism behavior of a single household consuming energy electric. Feedback between magnitudes revealed electricity consumption and its price on a specific farm home. The recipients of the work will be electricity sellers, consumers, and researchers of household market behavior. [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]

Copyright of Invest Journal of Sharia & Economic Law is the property of Invest Journal of Sharia & Economic Law 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.)

Under the urban development trend of sprawl, improving energy use efficiency is a proper way to promote green and low-carbon construction in cities. This paper uses panel data from 283 prefecture-level and above cities in China from 2008 to 2019 to measure the urban sprawl index, and analyze the spatial-temporal evolution law of urban sprawl and electricity consumption. The relationship between urban sprawl and electricity consumption is empirically examined, and the differential effect of urban sprawl on electricity consumption is analyzed. Finally, the impact of urban sprawl on electricity consumption based on a spatial perspective is explored in depth by establishing a spatial error model. We found the following: (1) The levels of urban sprawl and urban electricity consumption are on the rise. The spatial distribution of urban sprawl is more dispersed, and cities with high electricity consumption levels are mostly concentrated in the eastern coastal areas. (2) Urban sprawl exacerbates electricity consumption, and this conclusion is still robust after a series of robustness tests were conducted and endogeneity issues were taken into account. In terms of the influence mechanism, urban sprawl mainly affects electricity consumption by changing the allocation of land resources, increasing the dependence on private transportation, and inhibiting green technology innovation. (3) The incremental effect of urban sprawl on electricity consumption is more pronounced in cities with high sprawl levels, weak environmental regulations, and low green innovation levels, as well as in west cities. (4) Urban sprawl and electricity consumption both have a significant positive spatial correlation. Electricity consumption of cities is not only related to their own regions but also influenced by the adjacent regions, and the spatial correlation is mainly reflected in the random error term. This paper deepens the understanding of the basic laws of urban sprawl affecting urban low-carbon development, which also has implications for new urbanization strategies and green development. [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]

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]

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]

To cope with the coronavirus disease (COVID-19), national or sub-national regions have carried out many powerful anti-pandemic measures such as locking down, which may improve their regional air quality. This paper examines the relation between regional air pollution and work resumption from a novel post-pandemic perspective. Using a unique panel dataset on China's detailed industrial electricity consumption, this paper does not find a positive relation between post-COVID-19 work resumption and regional air pollution during China's early-stage recovery. This result is obtained after controlling for province and date fixed effects, as well as local weather conditions. However, the positive relations are found in a particular sub-sample of large industrial enterprises and a particular sub-sample of April. These findings indicate that large industrial enterprises may recover first, and the resumption is progressing gradually. Finally, several policy implications are provided, which are essentially helpful for other countries' post-pandemic recovery. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power is the property of Electric Power Editorial Office 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.)

In railway signaling design, the calculation of electricity consumption for railway signal equipment is very complicated. To simplify the process of electricity consumption design for railway signal equipment, this paper analyzes various calculation methods of signal load, and uses the VB6.0 programming software to compile the electricity consumption calculation software for railway signal equipment, reducing the difficulty of electricity consumption design for railway signal equipment and improving design efficiency. [ABSTRACT FROM AUTHOR]

With technological development and the decrease in the price of electrical appliances for domestic consumers, combined with users' desire for a certain level of comfort, the number of electrical devices in homes has increased. Although current equipment is largely energy-efficient, the high number of these devices, used simultaneously, has led to an increase in electricity consumption. Electrical installation in homes has been replaced in most cases, but the connections have remained the same, without any checks being carried out in terms of load and operational safety. This paper presents an exploratory case study based on a questionnaire answered by 678 individuals from Romania. The objectives of the questionnaire were to identify the characteristics of the domestic consumer and their opinion on the state of the installation, energy costs, and energy consumption. Based on the analysis, 74.15% of the respondents stated that the power installed in their homes as domestic consumers increased. Based on the analysis conducted on-site, the risk of fire outbreaks in the power supply columns of apartment buildings was identified. To avoid critical situations, it is necessary to periodically check the connection and supply columns so that they are functional and safe to operate. [ABSTRACT FROM AUTHOR]

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]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny 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.)

Electric smart grids enable a bidirectional flow of electricity and information among power system assets. For proper monitoring and controlling of power quality, reliability, scalability and flexibility, there is a need for an environmentally friendly system that is transparent, sustainable, costsaving, energy-efficient, agile and secure. This paper provides an overview of the emerging technologies behind smart grids and the internet of things. The dependent variables are identified by analyzing the electricity consumption patterns for optimal utilization and planning preventive maintenance of their legacy assets like power distribution transformers with real-time parameters to ensure an uninterrupted and reliable power supply. In addition, the paper sorts out challenges in the traditional or legacy electricity grid, power generation, transmission, distribution, and revenue management challenges such as reducing aggregate technical and commercial loss by reforming the existing manual or semi-automatic techniques to fully smart or automatic systems. This article represents a concise review of research works in creating components of the smart grid like smart metering infrastructure for postpaid as well as in prepaid mode, internal structure comparison of advanced metering methods in present scenarios, and communication systems. [ABSTRACT FROM AUTHOR]

Electricity Consumption is one among widely studied section of computer architecture for more than decades. Electricity Adoption is one of the parameter in Machine Learning. It is one of the emerging field in the research. It keeps an eye on high accuracy without any kind of computation constraint. The paper has the objectivity to analyze Machine Learning algorithms effectiveness, after being applied to the electricity consumption prediction. Load management and demand response, high dimensional data sets are much effective variable selection, accurate prediction for electricity market pricing. It retains economic mechanisms to the largest. Our goal is about important guidelines offering to the Machine Learning community and provide basic knowledge of building specific electricity consumption estimation methods for machine learning algorithms. This Paper reviews about the Conventional Machine Learning Models as well as the recent models, allowing predicting electricity consumption. A number of research works are concerned with the set of structural models and its corresponding applicabilities are introduced. The predictions are proposed for the research reference, depending upon previous work analogy. [ABSTRACT FROM AUTHOR]

This paper empirically examines electricity consumption–economic growth nexus in Uganda from 1981 to 2013. Autoregressive distributed lag-bounds testing and Granger causality tests are employed for analysis. The results confirm the existence of long-run relationship between electricity consumption and economic growth and vice versa. In addition, the Granger causality test results confirm conservation hypothesis in the short run and feedback hypothesis in the long run. Therefore, the paper recommends Ugandan authorities not only to develop energy policies geared toward promoting efficient energy use but also to expand electricity infrastructure to address increased electricity demand to support economic growth. [ABSTRACT FROM PUBLISHER]

Night‐time light (NTL) satellite imagery can provide unique insights into the energy sector. Nevertheless, there are limited studies that have systematically reviewed the literature on the relationship between electricity consumption and NTL. Therefore, this paper aims to provide a systematic review of studies that have explored this relationship. The review identified over 200 regression models estimating electricity consumption using NTL satellite images. The key finding of the review was that there was a large variability in regression performance for model prediction of electricity consumption from NTL imagery, indicating a need for further work to refine the techniques and approaches in this emerging field of remote sensing research. The level of spatial aggregation had an important influence on model performance with larger geographical areas, such as countries or states, providing better estimations. [ABSTRACT FROM AUTHOR]

Over the past years, the economies of the Gulf Cooperation Countries (GCC) countries, which hold a large of the world in oil and gas exports, have suffered from dramatic declines in oil prices. This has put pressure on their policymakers to seek relief from their heavy dependence on natural resources and diversify their exports. At the same time, the GCC countries have high electricity consumption due to their severe climatic conditions. This has prompted their policymakers to consider the inevitable shift to environmentally friendly resources for electricity generation. Foreign direct investment, facilitated by the financial systems of these countries, may play an important role in achieving the above objectives. Therefore, the objectives of our paper are to investigate the relationship between foreign direct investment, electricity consumption, and financial development in GCC countries using a novel GMM-Panel VAR approach. Data are observed from 1990 to 2019. We find bidirectional causality between foreign direct investment and financial development and two unidirectional paths: one from electricity consumption to foreign direct investment and from financial development to electricity consumption. In addition, we observe negative and significant responses of financial development to shocks in electricity consumption. Our results can have a double interpretation. First, they provide an overview of the interactions between the variables studied, whose evolution may be prognosticated with the analysis of shocks resulting from the methodology adopted in our paper. This is serviceable given the recurrent turbulence in the international financial markets and the incessant perturbations in the prices of energy commodities, visually examined especially in recent years. Second, our empirical findings are useful for policymakers willing to have earlier diagnoses for policies strengthening renewable energy use in these countries. Our results can be seen on the side of this objective. The quite developed financial system in these countries may play, indeed, an important role in encouraging the FDI installation in the field of renewable energy. [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 investigates the impact of the popularization and usage of the Internet on household electricity consumption in China, as well as the mediating role of sleep duration. By employing data from the China Family Panel Studies (CFPS) and employing the basic ordinary least squares (OLS) model, the mediation model, and the instrumental variable (IV) approach, we derive the following conclusions. The results from the basic OLS regression indicate a positive relationship between internet usage and household electricity expenditure, implying that households that use the Internet tend to have higher electricity bills. Subsequently, by introducing sleep duration as a mediating variable, we find that internet usage leads to shorter sleep duration, indirectly resulting in increased household electricity costs. To address potential endogeneity concerns, we employ the instrumental variable approach to correct for the impact of internet usage on household electricity consumption. In addition, through heterogeneity analysis, we found that internet usage impacts households with different characteristics. [ABSTRACT FROM AUTHOR]