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With the peak of carbon dioxide emissions and carbon neutrality, China is placing greater emphasis on energy expenditure. Office buildings occupy a prominent position in building energy consumption, which is one of the main energy consumption areas. Taking an administration building in Chengdu as an example, this article simulates the building energy consumption based on Design Builder software, examines the variables influencing energy consumption, and suggests energy-saving strategies combined with fresh ideas for sustainable architectural design. The results showed that the modeling building was a high-energy-consuming building, with an energy consumption of 724,857.59 kWh, and a unit area energy consumption of 288.17 kWh/m2 in Chengdu. For energy conservation and emission reduction, this article proposes the following three energy-saving measures. The first is to apply heat recovery technology for air conditioning systems. The second is photovoltaic glass, which provides partial electricity demand for buildings and reduces dependence on traditional energy sources. The third is roof greening, which utilizes the plants to purify the air and beautify the environment. The results showed that the heat recovery technology in air conditioning systems reduced the total energy consumption of buildings from 642144.04 kWh/m2 to 502937.83 kWh/m2, photovoltaic glass reduced 552243.87 kWh/m2, and roof greening reduced to 635947.35 kWh/m2. All of these have good energy-saving and emission reduction effects. The above three strategies not only help reduce building energy consumption, but also provide substantial support for China to achieve carbon neutrality. [ABSTRACT FROM AUTHOR]

Accurately predicting industrial electricity consumption is of paramount importance for optimizing energy management and operational efficiency. Traditional forecasting approaches face significant challenges in capturing the complex factors influencing industrial electricity consumption, often due to the inadequate representation of correlations, thus limiting their predictive capabilities. To overcome these limitations, we propose a novel graph-based forecasting model termed Industry–Geography Time Series Forecasting Model (IG-TFM). Our approach leverages historical electricity consumption data and geographical information relevant to similar industries to construct an industry–geography relationship graph. This graph serves as the foundation of a comprehensive network that encompasses all industries of interest, allowing us to identify sectors closely associated with the target industry. The structured graph data are then processed within a graph convolutional neural network framework, which effectively captures the impact of geographical location, industry similarities, and inter-industry relationships on electricity consumption patterns. Utilizing this enriched representation, we develop our IG-TFM for accurate time series forecasting of industrial electricity consumption. Experiments conducted on real-world data, including 31 industries across 9 cities in a southern province of China, demonstrate the significant advantages of our proposed method across key performance indicators such as the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). These findings underscore the importance and efficacy of employing complex networks to encode sequence-related information, thereby substantially improving prediction accuracy in industrial electricity consumption forecasting. [ABSTRACT FROM AUTHOR]

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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 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]

The development of smart cities will require the construction of smart buildings. Smart buildings will demand the incorporation of elements for efficient monitoring and control of electrical consumption. The development of efficient AI algorithms is needed to generate more accurate electricity consumption predictions; therefore; anomaly detection in electricity consumption predictions has become an important research topic. This work focuses on the study of the detection of anomalies in domestic electrical consumption in Mexico. A predictive machine learning model of future electricity consumption was generated to evaluate various anomaly-detection techniques. Their effectiveness in identifying outliers was determined, and their performance was documented. A 30-day forecast of electrical consumption and an anomaly-detection model have been developed using isolation forest. Isolation forest successfully captured up to 75% of the anomalies. Finally, the Shapley values have been used to generate an explanation of the results of a model capable of detecting anomalous data for the Mexican context. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) of heterogeneous multi-agents plays a significant role in smart grid. Faced with special difficulties of multi-agent STLF due to the high heterogeneity, uncertainty and volatility, traditional local methods usually make predictions based on load aggregation or clustering, the complexity of which will rise greatly with the increase of agent types. It has become more and more difficult for traditional methods to meet the STLF demand of smart grid. Meanwhile, global time series forecasting method emerges gradually in many fields, which can make predictions for many agents only by one model with much lower complexity. However, there is few researches on the multi-agent global STLF. This study proposes a global deep-Long Short-Term Memory (LSTM) STLF model based on a pretraining method. The model fully applies the information such as electricity consumption pattern, weather and calendar in a structural and orderly way. The empirical results show that our model can effectively predict the daily load of heterogeneous households, with prediction accuracies of 87.9–90.2% on the test set across various tasks. Compared with the base model, our model achieves an 8.7% higher accuracy with a much faster convergence speed. Furthermore, the fluctuations of accuracy on different tasks are within 2.3%, showing that our model is robust. The superiority of the global method is proved through the comparison with local method in the empirical results. This study is expected to make contribution to global STLF of heterogeneous households and providing experience for the global prediction of heterogeneous time series based on deep-LSTM and pretraining method. [ABSTRACT FROM AUTHOR]

Due to the diversity and randomness of residential consumption behavior, as well as the different share of domestic electricity demand by appliances, class imbalance problems exist in the non‐intrusive load monitoring (NILM) system. The recognition model becomes biased toward the majority class, which makes the recognition of the minority class difficult. To tackle this challenge, we propose a novel NILM framework for appliance recognition to overcome the insufficient learning problems of the minority class, which combines a multi‐domain feature extraction module, a two‐stage feature selection, and a data oversampling method. Multiple features are extracted to make full of the complementarity of combined features to enhance appliance recognition. Furthermore, a two‐stage feature selection based on minimal‐redundancy‐maximal‐relevance (mRMR) and random forest (RF) is developed to select an optimal feature set. Subsequently, an oversampling method combining K‐medoids and the synthetic minority oversampling technique (KM‐SMOTE) with sampling weights is proposed for data augmentation of the minority class to handle imbalance learning problems. The effectiveness of the proposed method is verified by the experiments on the public dataset PLAID. The results show that, compared with the state‐of‐the‐art techniques, the overall recognition accuracy and F1‐score with the proposed method are enhanced by 4.61% and 2.04%, respectively. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Globally, the carbon footprint (CF) is constantly increasing, contrasting with the decreasing trend observed for decades in the European Union (EU) countries, where EU guidelines are responsibly followed and outlined in its strategic documents. Information and communication technology (ICT) carbon emissions have historically increased in parallel with global emissions, contributing to continuous increases in ICT's CF over time, even when excluding full life cycle emissions. This study examines the impact of ICT on household electricity consumption, aiming to quantify the potential reduction of greenhouse gas (GHG) emissions through improved household energy efficiency. The methodology includes the data collection on ICT device usage in households within the city of Novi Sad (Republic of Serbia), employing the survey method that queries respondents on device quantities and their usage patterns. This study provides results for decision-makers to recognize concrete benefits from the transition to a circular economy (CE) and low-carbon emissions, which are reflected as benefits for the local community and socio-economic environment. [ABSTRACT FROM AUTHOR]

Electricity consumption pattern recognition is the foundation of intelligent electricity distribution data analysis. However, as the scale of electricity consumption data increases, traditional clustering analysis methods encounter bottlenecks such as low computation speed and processing efficiency. To meet the efficient mining needs of massive electricity consumption data, in this paper a parallel processing method of the density-based k-means clustering is presented. First, an initial cluster center selection method based on data sample density is proposed to avoid inaccurate initial cluster center point selection, leading to clustering falling into local optima. The dispersion degree of the data samples within the cluster is also used as an important reference for determining the number of clusters. Subsequently, parallelization of density calculation and clustering for data samples were achieved based on the MapReduce model. Through experiments conducted on Hadoop clusters, it has been shown that the proposed parallel processing method is efficient and feasible, and can provide favorable support for intelligent power allocation decisions. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to assess user awareness of electricity consumption at a public university in Ghana based on the norm activation model (NAM). Design/methodology/approach: The study adopted a survey design, using closed-ended questionnaires as the main data collection instruments. Data was collected from 300 respondents (staff and students) of the university and analysed descriptively using Statistical Package for the Social Sciences. Findings: The study found that the level of user awareness (as defined by NAM) of electricity consumption among staff and students is moderate. Therefore, the more energy campaigns users are exposed to the higher their level of user awareness (awareness of consequence, ascription of responsibility and personal norm). Practical implications: The findings of this study can assist university management boards and other organisations in recognising the potential for reducing electricity use by increasing user knowledge. Ultimately, this can result in added value. Originality/value: The study makes a significant contribution to the literature on the NAM by exclusively focusing on the variables of NAM to assess the level of user awareness of electricity consumption in a university setting. [ABSTRACT FROM AUTHOR]

The most common problem faced by modern metallurgical enterprises is the improvement of their energy efficiency, which is based on the management of energy-saving projects. The paper deals with the analysis of the impact of external noise on recurrent diagrams based on short-term time series of daily energy consumption of a metallurgical enterprise. The object of this study is short time series of energy consumption of a metallurgical enterprise. The time series of energy consumption of PJSC «Electrometallurgical Plant «Dniprospetsstal» (Ukraine) for 2018–2021 were used as data. The subject of the study is the method of recurrent diagrams of short time series. In the process of research, methods of short time series analysis based on recurrent analysis were used to study the characteristics of the system state on the example of a metallurgical enterprise. An analysis of the influence of external noise on recurrent diagrams of short-term chaotic time series was carried out using the developed software in the Matlab environment for constructing recurrent diagrams of energy consumption of a metallurgical enterprise. The following tasks were solved in the work: software was developed for constructing recurrent diagrams in the Matlab package with the possibility of analyzing changes in the magnitude of quantitative indicators of recurrent diagrams under the influence of different levels of noise in time series. The obtained results are recommended to be used to characterize the state of the system and analyze the influence of external noise. The practical value of the performed work is determined by the proven usefulness of recurrent analysis for estimating electricity consumption and the improvement of modeling of this process, which will allow increasing the accuracy of forecasting future dynamics verified by empirical data. [ABSTRACT FROM AUTHOR]

This paper investigates the asymmetric demand for clean energy for a panel of OECD countries. We construct a price index for clean energy based on information on the energy composition of electricity generation, and focus on the possibility of non-symmetric clean and dirty clean consumption due to rising and falling energy prices, and to positive income growth and negative income growth. Using an Autoregressive Distributed Lag (ARDL) model that encapsulates both long-run equilibrium among energy demands, energy prices and income, and short-run dynamics. We find evidence that clean energy demand adjusts more aggressively to a price decline than a price rise. In addition, we find that clean energy consumption is more responsive to negative income changes than to positive income changes. A climate policy implication is that policy should account for reversal effect of clean energy consumption during periods of economic downturn. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for electricity consumption profiling in households through the fusion of usage data for individual smart devices. The novelty of the approach consists of leveraging the data representing the usage of individual appliances rather than using direct measurements of energy consumption. Our methodology focuses on merging signals representing the interaction of the user with the device to compute patterns in the total energy consumption per household. Subsequently, we apply data mining techniques—specifically, unsupervised clustering—to analyze the resulting time-series data representing daily energy consumption. Through this approach, we aim to identify and characterize patterns in energy usage within households, enabling insights for energy optimization strategies and resource allocation. This information can be further used in practical tasks, such as flattening energy consumption. The proposed approach offers an alternative to the direct measurement of energy usage, considering the potential for sensor failure or malfunction. This underscores the importance of implementing a complementary method for verifying and validating energy consumption data. [ABSTRACT FROM AUTHOR]

The non-technical losses caused by abnormal power consumption behavior of power users seriously affect the revenue of power companies and the quality of power supply. To assist electric power companies in improving the efficiency of power consumption audit and regulating the power consumption behavior of users, this paper proposes a power consumption anomaly detection method named High-LowDAAE (Autoencoder model for dual adversarial training of high low-level temporal features). High-LowDAAE adds an extra "discriminator" named AE3 to USAD (UnSupervised Anomaly Detection on Multivariate Time Series), which performs the same function as AE2 in USAD. AE3 performs the same function as AE2 in USAD, i.e., it is trained against AE1 to enhance its ability to reconstruct average data. However, AE3 differs from AE2 because the two "discriminators" correspond to the high-level and low-level time series features output from the shared encoder network. By utilizing different levels of temporal features to reconstruct the data and conducting adversarial training, AE1 can reconstruct the time-series data more efficiently, thus improving the accuracy of detecting abnormal electricity usage. In addition, to enhance the model's feature extraction ability for time-series data, the self-encoder is constructed with a long short-term memory (LSTM) network, and the fully connected layer in the USAD model is no longer used. This modification improves the extraction of temporal features and provides richer hidden features for the adversarial training of the dual "discriminators". Finally, the ablation and comparison experiments are conducted using accurate electricity consumption data from users, and the results show that the proposed method has higher accuracy. [ABSTRACT FROM AUTHOR]

With the deteriorating environment and excessive consumption of primary energy, solar energy has become used in buildings worldwide for renewable energy. Due to the fluctuations of solar radiation, a solar photovoltaic (PV) power system is often combined with a storage battery to improve the stability of a building's energy supply. In addition, the real-time energy consumption pattern of the residual houses fluctuates; a larger size for a PV and battery integrated system can offer more solar energy but also bring a higher equipment cost, and a smaller size for the integrated system may achieve an energy-saving effect. The traditional methods to size a PV and battery integrated system for a detached house are based on the experience method or the traversal algorithm. However, the experience method cannot consider the real-time fluctuating energy demand of a detached house, and the traversal algorithm costs too much computation time. Therefore, this study applies Nondominated Sorting Genetic Algorithm-II (NSGA-II) to size a PV and battery integrated system by optimizing total electricity cost and usage of the grid electricity simultaneously. By setting these two indicators as objectives separately, single-objective genetic algorithms (GAs) are also deployed to find the optimal size specifications of the PV and battery integrated system. The optimal solutions from NSGA-II and single-objective GAs are mutually verified, showing the high accuracy of NSGA-II, and the rapid convergence process demonstrates the time-saving effect of all these deployed genetic algorithms. The robustness of the deployed NSGA-II to various grid electricity prices is also tested, and similar optimal solutions are obtained. Compared with the experience method, the final optimal solution from NSGA-II saves 68.3% of total electricity cost with slightly more grid electricity used. Compared with the traversal algorithm, NSGA-II saves 94% of the computation time and provides more accurate size specifications for the PV and battery integrated system. This study suggests that NSGA-II is suitable for sizing a PV and battery integrated system for a detached house. [ABSTRACT FROM AUTHOR]

This study focuses on carbon emissions of the building sector in relation to local climate zone (LCZ) classification, concentrating on two major parts. First, we estimated carbon emissions in the building sector, which were calculated for weekdays and weekends real-time daily energy consumption patterns. The estimations were divided into direct (from petroleum products consumption) and indirect emissions (from electricity consumption). Second, we examined urban carbon emissions mapping in relation to LCZ. Bangkok Metropolitan Administration (BMA) was used as the case study and 2016 as the base year for examination. The results illustrate that indirect emissions in Bangkok can be up to ten times higher than direct emissions. The analysis indicates that LCZ, such as compact high-rise, large low-rise, light industry, and warehouse zones had a relatively higher carbon emission intensity than others. Additionally, we identified that the compact high-rise zone has the highest indirect emission intensity, while the light industry and warehouse zone have the greatest direct emission intensity. These results provide insights into the dynamics of carbon emission characteristics in the building sector and the methodology purported here can be used to support low carbon city planning and policymaking in Bangkok. [ABSTRACT FROM AUTHOR]

Load forecasting is crucial for the operation and planning of electricity generation, transmission, and distribution. In the context of short‐term electricity load prediction for residential users, single‐task learning methods fail to consider the relationship among multiple residential users and have limited feature extraction capabilities for residential load data. It is challenging to obtain sufficient information from individual residential user load predictions, resulting in poor prediction performance. To address these issues, we propose a framework for multi‐task residential short‐term load prediction based on contrastive learning. Firstly, clustering techniques are used to select residential users with similar electricity consumption patterns. Secondly, contrastive learning is employed for pre‐training, extracting trend and seasonal feature representations of load sequences, thereby enhancing the feature extraction capability for residential load Feature. Lastly, a multi‐task learning prediction framework is utilized to learn shared information among multiple residential users' loads, enabling short‐term load prediction for multiple residences. The proposed load prediction framework has been implemented on two real‐world load data sets, and the experimental results demonstrate that it effectively reduces the prediction errors for residential load prediction. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

With the advancement of smart power plants, electricity consumers are transitioning from a passive electricity consumption model to an intelligent consumption model. However, distributed power equipment and the vast amounts of electricity consumption data they generate pose significant information security risks. Traditional access control mechanisms cannot meet the security needs of distributed smart power plants. To address this issue, this paper introduces a distributed smart power plant access control mechanism based on blockchain and ciphertext updatable functional encryption. Firstly, the decentralized and tamper-proof features of blockchain technology ensure the integrity, transparency, and security of data transmission, effectively preventing data tampering and leakage. Secondly, by incorporating ciphertext updatable functional encryption, we guarantee data confidentiality and achieve fine-grained access control over electricity consumption data. This allows users to independently and precisely control data sharing and access. Furthermore, by deploying smart contracts with trust mechanisms on the blockchain, automated management of storage and access is achieved. Through a comprehensive evaluation of trust levels, trust values, and honesty, the system not only enhances its efficiency but also ensures that only legitimate entities can access data resources, thus preserving the utility of electricity consumption data. In addition, the adoption of decentralized unique identifiers on the blockchain for user authentication and authorization strengthens user privacy protection and data sovereignty. Lastly, experimental results demonstrate that this mechanism, while ensuring security, maintains a high level of efficiency, meeting the complex security requirements of distributed smart power plants. [ABSTRACT FROM AUTHOR]

Purpose: On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data. Design/methodology/approach: First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption. Findings: Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability. Originality/value: This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability. Highlights: The stability of electricity consumption is important to the stable operation of the grid. An improved FP-growth algorithm is employed to explore the influencing factors. The improved algorithm enables the mining of rules containing specific attribute labels. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use. [ABSTRACT FROM AUTHOR]

Non-Intrusive Load Monitoring (NILM) is a valuable technique for breaking down overall power consumption into the energy usage of individual appliances. Understanding power usage patterns through NILM plays an important role in reducing energy costs and achieving carbon reduction goals. However, privacy concerns often deter consumers from sharing their electricity consumption data. To address these privacy concerns, Federated Learning (FL) has been introduced in NILM, which enables the training of NILM models while keeping power consumers' data locally. However, FL's reliance on a single global model leads to poor performance on clients with unique power consumption patterns. In response to this challenge, we present a Personalized Federated Learning NILM algorithm (PerFedNILM), a practical personalized FL approach for NILM. PerFedNILM limits the local update bias across clients and trains personalized models for individual clients to improve load-monitoring performance. In addition, it mitigates the negative impact of client dropout, which is a common issue in practice. Our experiments on using real-world energy data demonstrate that PerFedNILM outperforms previous FL-based NILM methods, especially in client dropout scenarios. [ABSTRACT FROM AUTHOR]

The growing availability of smart meter data has facilitated the development of energy-saving services like demand response, personalized energy feedback, and non-intrusive-load-monitoring applications, all of which heavily rely on advanced machine learning algorithms trained on energy consumption datasets. To ensure the accuracy and reliability of these services, real-world smart meter data collection is crucial. The Plegma dataset described in this paper addresses this need bfy providing whole- house aggregate loads and appliance-level consumption measurements at 10-second intervals from 13 different households over a period of one year. It also includes environmental data such as humidity and temperature, building characteristics, demographic information, and user practice routines to enable quantitative as well as qualitative analysis. Plegma is the first high-frequency electricity measurements dataset in Greece, capturing the consumption behavior of people in the Mediterranean area who use devices not commonly included in other datasets, such as AC and electric-water boilers. The dataset comprises 218 million readings from 88 installed meters and sensors. The collected data are available in CSV format. [ABSTRACT FROM AUTHOR]

In light of the escalating concerns surrounding climate change and air quality degradation, the imperative for energy conservation and emission reduction has garnered widespread attention. Given that factories represent a significant portion of electricity consumption within the power network, a comprehensive analysis of the electricity consumption behavior of energy- intensive enterprises becomes paramount. To meticulously dissect the electricity consumption patterns of energy-intensive enterprises, this paper categorizes them into four distinct production modes: 24-hour all-day production factories, pure daytime production factories, pure nighttime production factories, and environmentally friendly peaking production factories. Employing the dynamic electricity-carbon factor as a guiding force, the analysis encompasses electricity consumption, tariff expenditure, peaking costs, carbon emissions, and comfort levels associated with each production method throughout the year. A delicate equilibrium is sought among multiple objectives, aiming to optimize the user experience while simultaneously mitigating costs and carbon emissions. Furthermore, this paper conducts a comparative analysis of each objective, employing single-objective genetic algorithms and the interior point method. The resultant findings serve as invaluable insights for business users, aiding in informed decision- making processes. [ABSTRACT FROM AUTHOR]

In the application of new energy consumption system engineering, in order to evaluate the contribution of electric power industry expansion, an evaluation model of electric power industry expansion contribution considering the influencing factors of new energy consumption is constructed. In the process of power industry expansion, the growth of new energy installed capacity, power system regulation ability, power grid interconnection and electricity demand are the core factors that affect the change of power contribution to power industry expansion. Using the characteristic extraction method of power consumption behavior of users with industrial expansion, after extracting two characteristics, namely, the utilization hours of user's industrial expansion capacity and the proportion of new energy load put into operation under the change of four major factors, the monthly industrial expansion power consumption of typical users is predicted by the monthly industrial expansion power consumption forecasting method of users considering industrial expansion capacity, and then the growth curve of user's industrial expansion power consumption is drawn. Based on the forecast method of monthly industry expansion electricity generated by industry expansion quantity, the industry expansion quantity of typical individual users is calculated, and the industry expansion quantity is input into RBF network model trained by particle swarm optimization algorithm to complete the forecast of monthly industry expansion electricity; Finally, the contribution ratio of each influencing factor is calculated, and the evaluation of power industry expansion contribution considering the influencing factors of new energy consumption is completed. After testing, this model can be used as an available model for evaluating the contribution of electric power industry under the condition of considering the influencing factors of new energy consumption. [ABSTRACT FROM AUTHOR]

Cooling equipment is widely used in industry, commerce and households. Due to their widespread use, they are responsible for the consumption of a significant amount of electricity. They are subject to degradation and various types of damage. Most often, their energy efficiency decreases and electricity consumption increases. Practice shows that even a specialist service is unable to diagnose damage at an early stage of its development. The paper presents a comparison of continuous monitoring of the temperature of the cooling chamber as a utility standard, with constant monitoring of the temperature of the cooling chamber and electricity consumption of a professional refrigeration cabinet with a built-in condensing unit. The comparative analysis was intended to confirm the thesis about unconscious waste resulting from assessing the correct operation of the device based on limited information. The experiment showed an increase in daily electricity consumption on average by over 30% during the period of unconscious exploitation of the device in a state of failure and an increase in daily electricity consumption on average above 300% during the period of conscious exploitation of the device in a state of failure, but still at an acceptable level of temperature of cooling chamber. [ABSTRACT FROM AUTHOR]

Mining and processing enterprises are large consumers of electricity, with a large share of energy costs in the cost of production. Thus, the price of electricity in the cost of production takes higher values for Hydrometallurgical Plant 1. In this regard, the development of energy consumption models that can be used as a basis for managing the energy consumption process is an important component of learning. It is important to analyze the energy consumption to estimate the electricity consumption under different operating conditions of the mining industry. In order to study and plan electricity consumption and develop methods for regulating electricity consumption, it is necessary to determine the place of each technological stage in the energy consumption balance [1]. This article examines the laws of electricity consumption in mining and processing enterprises. The study of such laws helps to determine the level of energy consumption management for the mining and processing plant. [ABSTRACT FROM AUTHOR]

The electrical energy expenditure of households in Turkey has been increasing over the years, and households account for one-fifth of the overall electrical energy consumption. For this reason, it is of vital importance to determine the factors that affect household electricity expenditure, reveal consumption patterns for different consumer groups, and propose suggestions for the efficient use of electrical energy. In the present study, determinants of household electricity expenditure were analyzed with the quantile regression approach using the Household Budget Survey published by the Turkish Statistical Institute. In conclusion, it was determined that demographic, economic, and residential factors had significant effects on the electricity expenditures of households. The present study will serve as a guide for policymakers and decision-makers in developing strategies and conducting studies toward reducing the electricity expenditures of households, which is an important factor for energy usage. [ABSTRACT FROM AUTHOR]

Electricity is an essential energy resource in the industrial, commercial and housing sector, having a very important role in the development of societies. Urbanization and industrialization implies a great demand of energy for developing economies. In the search to be able to know how much electrical energy is consumed, a modeling of the electrical energy demand is carried out. However, the inherent intricacy and nonlinear nature of electricity consumption patterns present a significant obstacle to achieve precise modeling. In this article, a chaos theory approach is carried out to analyze the behavior of the system and to obtain properties of its dynamic system. A network consisting of a convolutional part, a hidden part and an output part is proposed. Convolutional operations are employed for dimensionality reduction in transformed data sets by reconstruction of the phase space. A radial basis function neural is used in the hidden part. The dynamic analysis approach using chaos theory, and the proposed neural network is compared with the radial basis function neural network for the modeling of electrical energy consumption. [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]

In this study, a prototype was developed for the effective utilisation of a domestic solar power plant. The basic idea is to switch on certain electrical appliances when the surplus of generated energy is predicted one hour in advance, for example, switching on a pump motor for watering a garden. This prediction is important because some devices (motors) wear out if they are switched on and off too frequently. If a solar power plant generates more energy than a household can consume, the surplus energy is fed into the main grid for storage. If a household has an energy shortage, the same energy is bought back at a higher price. In this study, data were collected from solar inverters, historical weather APIs and smart energy meters. This study describes the data preparation process and feature engineering that will later be used to create forecasting models. This study consists of two forecasting models: solar energy generation and household electricity consumption. Both types of model were tested using Facebook Prophet and different neural network architectures: feedforward, long short-term memory (LSTM) and gated recurrent unit (GRU) networks. In addition, a baseline model was developed to compare the prediction accuracy. [ABSTRACT FROM AUTHOR]

The State of Mexico is a Mexican entity facing socio-environmental problems: destruction of forest resources, water and air pollution, and poor quality of basic services. This paper analyzes its socio-urban metabolism between 2010 and 2020, considering it as a thermodynamic machine. Using Material Flow and Energy Flow Analysis (MFAE), indicators of consumption and emissions of electricity, fuel, water and social aspects (schooling, social security, economically active population and PBT) were obtained. Metabolic Indexes (MI) were calculated to indicate consumption by municipality. The results showed increases in: fuel consumption (275%), water (130%) and decrease in electricity consumption (-65.7%) due to costs, overcrowding and theft, indicating a governmental inability to provide services. On the social issue, the quality of education declined despite the decrease in the illiterate population. Access to social security rose (27%) in 2020, but public entities are bankrupt and 47.9% of the population is poor. The MI showed that the most consuming municipalities are those in the Toluca Valley and those in the suburbs of Mexico City. Despite the alignment of urban planning to the SDGs, they are not achieved due to methodologies that do not contemplate thermodynamic laws, socio-environmental complexity and, therefore, long-term sustainability. [ABSTRACT FROM AUTHOR]

With rapid global economic growth and a rise in disposable household income, particularly within a progressively warming planet, the escalating demand for energy to achieve thermal comfort has become a salient concern in the Global South, notably in emerging economies like India. This burgeoning need for cooling solutions has not only underscored the vital role of energy consumption but has also accentuated the imperative of comprehending the ensuing implications for electricity policy and strategic planning, particularly within the ambit of the Global South. This study explored the nuanced landscape of active cooling within an intentional community, Auroville, in southern India, aiming to discern the factors underpinning household preferences and practices in the pursuit of thermal comfort. Employing a mixed-methods approach, this study contributed empirically and methodologically to the interdisciplinary discourse by analysing residential electricity consumption patterns and cooling practices within selected households in the specified community. The study unfolded in three methodological stages: firstly, an analysis of climatic data coupled with an environmental stress index (ESI) assessment; secondly, the monitoring of end-user electricity consumption followed by rigorous data analysis; and lastly, the utilisation of qualitative in-depth interviews and observational techniques. This study's outcome yielded empirical insights into the unprecedented shifts in the ESI for Auroville since 2014. Furthermore, the study unravelled the intricate complexities inherent in occupant behaviour within residential structures, thereby offering valuable insights into the practices that shape householders' cooling preferences. This research enriched the understanding of the dynamics of energy consumption in the pursuit of thermal comfort and contributes to the broader discourse on sustainable development and energy policy in the context of climate change. [ABSTRACT FROM AUTHOR]

In this study, it is aimed to determine the relationship between the variables and to analyze the effect of independent variables on electricity consumption by using data on electricity consumption, economic growth, foreign direct investment, and general price level for the period 1996-2022 In the study, the cointegration relationship is analyzed using the Autoregressive Distributed Lag bounds test approach. The Fourier-Toda-Yamamoto causality test has been applied. Short- and long-term effects have been revealed. It is observed that energy consumption and economic growth have a positive relationship with the same direction in the short and long term. Inflation has a negative impact on energy consumption in the short and long term. In addition, the increase in foreign direct investments in the short term leads to a significant increase in energy consumption in the relevant period in Turkey. The study, which applies the Fourier-Toda-Yamamoto causality test, finds that there is no causality between electricity consumption, Gross Domestic Product and Foreign Direct Investment variables. [ABSTRACT FROM AUTHOR]

This article examines the relationship between electricity consumption and the stock market in the Turkish economy during the COVID-19 pandemic. A novel high-frequency model is used, incorporating the hourly energy consumption and Borsa Istanbul (BIST) National stock market index variables. To determine the effect of electricity consumption on the stock market index and vice versa, a high-frequency VAR-based spillover approach, time-varying Granger causality, and time-varying Bayesian VAR analysis are employed. The findings reveal a positive and weak relationship between electricity consumption and the stock market but it has a time-varying nature in an emerging market context in the post-COVID-19 period in the Turkish economy. [ABSTRACT FROM AUTHOR]

Based on survey data from Chinese households, this study uses a fixed effects model to examine the impact of car ownership on household energy consumption. The results show that car purchases lead to a significant 46% increase in household energy consumption costs. Purchasing cars leads to a 108% increase in household transportation expenditure (including gasoline expenditure), showing a significant direct effect, while heating costs increase by 17.1%, 12.0%, and 23.3%, respectively, representing an indirect effect. Furthermore, in regions with rapid EV growth, EVs have alleviated the promotion of cars in energy consumption, which is conducive to achieving the goal of carbon neutrality. This study helps provide measures to reduce future household energy consumption and promote the realization of carbon neutrality. [ABSTRACT FROM AUTHOR]

With Vietnam's countrywide electrification rate approaching 100 percent, electricity has become ever more indispensable and essential to the quality of everyday life. The steady upsurge in household electricity consumption over the past years has been driven by a growing technification of the domestic sphere. Modern households are increasingly equipped with a variety of electric appliances that not only provide convenience and comfort but also serve as markers of social distinction. This essay explores the history of domestic electrification and people's aspirations to live electrically in urban Vietnam, from the colonial era until today. Inspired by recent advances in the anthropology of electricity, it investigates the meanings that people have attributed to electric home appliances through the ages and asks if, how, and why some of these have come to define what it means to live a good life. In the current era, however, living electrically has also come to reflect a global sense of "cruel optimism" in which people become increasingly attached to certain understandings of what constitutes a good life that are unsustainable in the long run, and detrimental to their well-being. [ABSTRACT FROM AUTHOR]

This study focuses on finding suitable installation sites for vertical bifacial photovoltaic (VBPV) panels in urban low-rise neighborhoods at high latitudes. The power production of east-west-oriented VBPV systems matches well with domestic electricity consumption profiles, increasing the self-consumption of PV electricity. Furthermore, PV electricity adds economic value by avoiding transmission fees and taxes. These systems are especially beneficial in high-latitude locations characterized by a low solar elevation angle. However, these low angles expose VBPV panels to a high risk of shading losses from their surroundings, and it is unknown how much shading limits the number of suitable installation sites. Here, environmental shading on VBPV panels is quantified for three low-rise residential neighborhoods in Helsinki, a high-latitude location, by comparing the specific yields (annual electricity production per kilowatt peak) of VBPV and monofacial PV (MPV) systems. The results showed that unshaded VBPV systems have a higher specific yield than their MPV counterparts. However, in densely built neighborhoods with tall trees, the lack of suitable installation sites for VBPV panels severely limits the peak power of these systems. Roof ridge VBPVs usually yield high production, while façade- and ground-mounted systems lose between 30% and 70% compared to roof ridge VBPV systems depending on their installation locations. South-oriented MPVs perform better than VBPVs on north-south-facing roofs, both in terms of specific yield and total annual production. Conversely, VBPVs installed on the ridges of unshaded roofs aligned closely with the north-south axis outperform MPVs on east- and west-facing roofs by 20%-30%. [ABSTRACT FROM AUTHOR]

Electrical energy consumption profiles provide a thorough picture of how energy is consumed across different energy users and at different intensities.. Electricity-consuming equipment may encompass a diverse range of sorts that are necessary for the execution of educational activities within a university. The process of conducting an electrical energy audit involves adhering to established standards for electrical energy audits. The determination of electrical energy consumption profiles in university academic operations has been accomplished by the analysis of previous energy usage data and measuring of electrical consumption quantities. The implementation of energy conservation measures significantly influenced the management of the Energy Performance Index (EPI) and electric power consumption profile at the CHARUSAT campus from 2018 to 2022. The evaluation results demonstrate that the Energy Performance Index (EPI) of the CHARUSAT Campus power remains within the criteria for electric energy conservation and is categorized as highly efficient, as per the norms established by the Bureau of Energy Efficiency in India. [ABSTRACT FROM AUTHOR]

The paper investigates the interplay between clean energy, environmental pollution reduction, and economic growth in the Gulf Cooperation Council (GCC) countries from 1980 to 2019, utilizing the autoregressive distributed lag (ARDL) method. The study underscores the global transition towards cleaner energy sources and its implications for the region. In assessing the determinants of economic growth, the findings reveal a positive and significant short- and long-term impact of energy production, with energy consumption exhibiting a positive and significant effect solely in the long term. The absence of a confirmed relationship in the short term is notable. Causality tests demonstrate a significant link from both energy consumption types to environmental pollution, alongside causal relationships from urbanization, energy production, and consumption to economic growth. Moreover, the results include the pivotal role of energy production in driving GDP growth, caution in short-term energy consumption effects, and significant causal links between energy consumption and environmental pollution. The recommendations to incentivize sustainable energy production, engage in long-term energy consumption planning, and adopt integrated urbanization policies provide actionable insights for policymakers. These suggestions are aimed at guiding the GCC countries in balancing the pursuit of economic growth with environmental sustainability—a delicate equilibrium that necessitates careful consideration of various factors. [ABSTRACT FROM AUTHOR]

Smart meters are devices that provide detailed information about the energy consumed by specific electricity supply points, such as homes, offices, and businesses. Data from smart meters are useful for modeling energy systems, predicting electricity consumption, and understanding human behavior. We present the first smart meter dataset from Spanish electricity supply points, expanding the geographic diversity of available data on energy consumption at the household level and reducing biases in existing data, which typically come from a limited number of countries. The dataset consists of 25,559 raw hourly time series with an average length of nearly three years, spanning from November 2014 to June 2022. It also includes three subsets obtained by segmenting and cleaning the raw time series data, each focusing on the periods before, during, and after the COVID-19 lockdowns in Spain. This dataset is a valuable resource for studying electricity consumption patterns and behaviors that emerge in response to different natural experiments, such as nationwide and regional lockdowns, nighttime curfews, and changes in electricity pricing. [ABSTRACT FROM AUTHOR]

In 1998, the red-green Schröder government implemented the Environmental Tax Reform (ETR), raising taxes on petrol, diesel, natural gas and heating oil and introducing a new duty on electricity in Germany. At the same time, it cut non-wage labour costs by reducing public pension contributions. The goal was to achieve Germany's Kyoto Protocol emissions targets and to reduce a level of unemployment unprecedented since World War II while avoiding the burden on the public budget through revenue recycling. Employing microdata from household budget surveys of 1998 and 2003, this article analyses whether increased duties on motor fuels and electricity lead to a substantial reduction in households' consumption of these goods. Considering the ETR as a natural experiment, it uses the difference-in-differences approach in a European context with Germany as the treatment group and Italy, Spain and the UK as the control group. Ordinary least square regressions reveal that motor fuel demand is price inelastic, while electricity consumption increased despite the substantial rise in prices. Quartile regressions show that the effect of the motor fuel tax is slightly higher at the bottom than at the upper tail of the distribution supporting the notion that low-level consumers are more likely to find alternative substitutes. [ABSTRACT FROM AUTHOR]

A hybrid workstyle is becoming more common post-COVID-19, and longer occupancy hours at home are increasing household electricity consumption. Small homes are regarded as a potential for improving energy efficiency in the residential sector, and a home consists of mixed-function spaces with dynamic occupancy behaviors. These underpin the opportunity to optimize presence sensing lighting in small homes for energy efficiency and user-behavioral needs. A comprehensive overview of presence sensing approaches, comparing four types of non-wearable sensors connected to home lighting is made. A bibliometric mapping of the reviewed literature visually reinforces a significant research gap that presence sensing studies were not connected to home lighting but inclined toward the commercial and institutional context. Next, a non-exhaustive example of commercially available presence sensing products applicable to residential lighting for small homes is analyzed, and their general characteristics and technologies are synthesized. The literature and product overview identified five significant product knowledge gaps. Incorporating the gathered information leads to the proposal of a conceptual flexible radar-based sensor (prototype design), addressing a wish list with three important criteria to optimize future presence sensing lighting in a mixed-function small home. Future radar sensing studies are expected to develop an anticipatory lighting system that processes real-time multi-user vital signals for smarter localized and personalized lighting options for (small) living environments. [ABSTRACT FROM AUTHOR]

With the large increase of load users, the load side adjustable resources gradually increase, and the load side demand response (DR) resources are used to participate in microgrid scheduling to improve the level of new energy consumption. In order to give full play to the scheduling potential of DR resources and optimize the user side load management capability, a multi-time scale optimal scheduling strategy for microgrids that takes into account scheduling priorities was proposed. Firstly, according to the response characteristics of different DR resources, the price-based demand response(PDR) resources and incentive-based demand response(IDR) resources were subdivided into five types, and a DR model was constructed to match the scheduling period. Secondly, the multi-times cale optimal scheduling model of "day-intra-day 1 h-intra-day 15 min" was constructed, optimized scheduling of various adjustable resources in the microgrid, and established priority weights for real-time adjustable resources that have a direct impact on users' electricity consumption behavior. Finally, taking an actual microgrid as an example in Fujian, the simulation verifies the validity of the model. [ABSTRACT FROM AUTHOR]

Traditional methods of planning and managing cities often adopt a simplified linear approach to understanding their composition and functioning. These approaches have led to the creation of many undesirable and non-functional cities. However, over the last few decades, there has been a radical shift in the approach to investigating cities through the adoption of complexity science and its various concepts. Unfortunately, this paradigm shift has been limited to only a handful of developed Western nations. This study proposes an extendable agent-based computational model for assessing household electricity consumption patterns in cities of developing nations. The model is demonstrated using an urban precinct in India as a representative case. It simulates the monthly electricity consumption of an urban precinct in Nagpur by combining the significant factors impacting household electricity consumption in developing nations, with the household’s electricity consumption process in relation to outdoor weather conditions, and the heterogeneity in occupants’ behavior. The predictive validation of the model using the “BehaviorSpace” tool yielded promising results, with the model consistently and accurately predicting the growth of variables and household electricity consumption patterns. The model also successfully generated multiple future scenarios, showcasing its capacity to forecast the future implications of policy interventions. The simulation results indicated that household electricity consumption in Indian cities is driven by population growth and income levels. Additionally, it suggests that widely practiced rooftop solar panel installation policies may not be a viable approach to achieving sustainability in this sector. This study finds that the “emergent urbanism” approach has the potential to revitalize the urban energy planning process through an effective framework for comprehensively understanding the phenomena and addressing its various complexities. [ABSTRACT FROM AUTHOR]