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Background: Demand response is an important option for accommodating growing shares of renewable electricity, and therefore, crucial for the success of the energy transition in Germany and elsewhere. In conjunction with smart meters, real-time (or 'dynamic') electricity tariffs can facilitate the flexibilization of power consumption and reduce energy bills. Whilst such tariffs are already quite common in several EU member states, Germany lags behind in this respect. The country makes for an interesting case study because of the sheer volume of additional flexibility that its energy transition necessitates. Main text: This paper discusses how German policymakers can make real-time tariffs more attractive for households and thus entice them to better adapt their consumption to current market conditions. Following an analysis of the current impediments to the adoption of such tariffs, we discuss four policy options: (1) a more ambitious legal definition of real-time tariffs that can promote market transparency and leverage potential savings for consumers, (2) a shift in energy taxation that encourages the uptake of renewable power and increases price spreads, (3) a new model of dynamic network charges which combines grid-serving and market-serving incentives, and (4) a subsidy for users of real-time tariffs that helps internalise the benefits they provide to all electricity consumers. Given the similar regulatory framework, our suggestions should generally also apply to other countries in Europe and beyond. Conclusions: Overall, we argue that there is considerable scope for policymakers to better exploit market forces to ensure security of electricity supply at lower social cost. Our call for stricter regulation in order to allow the markets to better guide consumer behaviour may seem like a paradox—but it is one well worth embracing. [ABSTRACT FROM AUTHOR]

Summary: For the sensor network, greedy forwarding is thought to be the best energy‐efficient routing method. There are two restrictions on greedy forwarding: a minimum local scenario and an intersection problem in which it is unable to locate an existing route. The proposed independent gradient forward (IGF) proves that the Euclidean equation used by greedy forwarding is less capable of dealing with the problems mentioned above. IGF considers each dimension independently to enhance the efficiency of greedy forwarding, sustaining its simplicity. It contributes to strengthening the suggested IGF's ability to manage holes and get around obstructions in the wireless sensor network. We have demonstrated that it travels a shorter potential path and avoids stuck nodes well in advance. It is more energy‐efficient since it can predict the void node and never visits a stalled node. We evaluated the suggested IGF's performance against other traditional boundary traversal‐based methods on both rare and dense networks. In comparison with other boundary traversal‐based algorithms, the simulation results indicate that it generates a 10% smaller route to bypass. The IGF average energy consumption increases slowly, rising by just 7% and 12.5% for dense and rare networks, respectively, with a fivefold increase in void size (from 100 to 500 m). [ABSTRACT FROM AUTHOR]

Fossil fuels remain indispensable energy resources despite their non-renewable nature. Understanding the patterns of global fossil fuel consumption is essential for energy security and policy-making. This study employs complex network theory and fractal time series analysis to explore the underlying dynamics and patterns of fossil fuel consumption globally, with a focus on coal, oil, and gas consumption.The study applies the Hurst index to raw fossil fuel consumption data to identify fractal characteristics. Additionally, the visibility graph method is used to convert time series data into complex networks, allowing further analysis of consumption patterns. The study examines fossil fuel consumption in 38 countries to assess global trends and differences. The analysis reveals that global fossil fuel consumption follows a fractal time series pattern, with Hurst index values exceeding 0.9, indicating long-term memory characteristics. The application of the visibility graph method demonstrates variations in the Hurst index of degree distribution, enabling the differentiation of consumption patterns across regions. The method also uncovers distinct features of coal, oil, and gas consumption when viewed from a network perspective. The findings suggest that fossil fuel consumption has predictable long-term patterns, which are crucial for assessing future energy demands. The study highlights the importance of legislative measures to safeguard fossil fuel resources, especially for countries like China, where energy security and international competitiveness are paramount. Understanding these consumption patterns could guide future energy policies aimed at managing non-renewable resources more effectively. [ABSTRACT FROM AUTHOR]

Office buildings are responsible for about 35% of the total electricity in the US and over 70% of building energy consumption occurs during occupancy periods. Therefore, understanding occupancy behavior is crucial for reducing building energy consumption. However, given the stochastic nature of occupant behavior, identifying which occupancy parameters have the most impact on energy consumption poses a considerable challenge. This study aims to investigate and quantify the impact of various occupancy parameters on the energy performance of a US small-sized office building using an EnergyPlus-based nationwide energy simulation. First, dynamic occupancy schedules are created based on different occupancy parameters using an agent-based model. Next, the generated dynamic occupancy schedules are integrated into a small office building model from the Department of Energy's prototypes. This creates a dataset of occupancy parameters and building energy performance across various climate zones. Finally, various feature selection and statistical analysis methods are applied to the generated dataset. This helps identify significant occupancy parameters and quantify their impact on building energy performance across different climate zones. According to the results of the study, buildings located in cool marine, mixed marine, and warm marine climate zones had lower total energy consumption compared to other zones. Additionally, feature selection methods identified "Occupant Density" as the primary significant variable impacting energy consumption, across all climate zones. These findings offer valuable insights into the influential occupancy parameters across various climate zones, highlighting the importance of tailoring occupancy schedules to enhance energy efficiency. They provide practical guidance that can be directly applied to optimize energy consumption and achieve significant energy savings in small office settings with different weather conditions. [ABSTRACT FROM AUTHOR]

The accurate prediction of China's energy consumption and CO2 emissions is important for the formulation of energy and environmental policies and achieving carbon neutrality. This paper proposes a new weighted error evaluation criterion that emphasizes the importance of new data, thereby enabling more accurate capture of the variation characteristics of new data and reflecting the principle of new information priority. By optimizing the development coefficient, grey action, and parameters in the time response formula of the traditional GM(1,1), a novel optimized model, OGMW(1,1), is constructed. The accuracy of the new model is verified by three cases from the literature. The future trends of primary energy, oil, and coal consumption and CO2 emissions in China are predicted over the next five years. The conclusions are as follows: First, the new weighted error evaluation criteria are effective and reasonable and can indicate whether a grey model can reliably use the most recent information for modeling. Second, based on the new error evaluation criteria, the development coefficient, ash action, and parameter C in the time response function can be optimized. The results show that the optimization method is reasonable. Third, compared with the traditional models GM, GMO, and ARIMA, the OGMW(1,1) provides better simulation and prediction accuracy, and new information can be prioritized more effectively. Fourth, the forecasting results indicate that China will increase its consumption of primary energy, oil, and coal, as well as its CO2 emissions. Notably, the growth rates of primary energy and oil consumption are high at approximately 22.7% and 25%, coal consumption will increase slightly, and CO2 emissions will increase by approximately 11%. [ABSTRACT FROM AUTHOR]

Ventilation plays a crucial role in preventing indoor airborne disease transmission. Nevertheless, ventilation increases the energy consumption of HVAC systems. Therefore, energy efficiency measures or alternative methods must be adopted to reduce the energy demand of HVAC systems, which is necessary to achieve sustainability in the building sector. This study proposes a method of utilizing an energy recovery ventilator (ERV) to provide supplementary ventilation to reduce airborne disease transmission. The proposed method is tested for an office building with one source room (with an infected occupant) and two connected rooms (no infection source). The contributions of the present study are (i) the development and verification of a new supplement ventilation method using an ERV to reduce the probability of infection from airborne pathogens and (ii) providing the economic and environmental benefits of the proposed method to promote its adaption by the building managers/HVAC engineers. The results of the present study show that the proposed method can reduce the probability of infection by 10 to 40% and demonstrate that utilizing an ERV is a sustainable and economical method to improve ventilation to reduce indoor airborne disease transmission. [ABSTRACT FROM AUTHOR]

This paper describes a comprehensive methodology for improving Wireless Body Area Networks (WBANs) in healthcare systems using Enhanced Gray Wolf Optimization (GWO). The methodology begins with WBAN initialization and the configuration of critical network parameters. To improve network performance and trustworthiness, direct trust calculations, historical trust , and energy trust, as well as energy consumption models based on distance and transmission type, are integrated. The use of an Enhanced GWO approach makes it easier to select optimal cluster heads, guided by a customized fitness function that balances trust and energy efficiency. This work has been carried on a PC with 16 GB RAM using MATLAB R2022b tool for simulation purpose. The methodology outperforms existing methods in terms of throughput, computation time, and residual energy. This promising methodology provides improved data routing, energy efficiency, and trustworthiness, making it a valuable asset in WBAN-based healthcare systems. [ABSTRACT FROM AUTHOR]

• Predicted power consumption and COP a chiller plant using XGBoost. • Quantified impacts of data intervals and data processing on the accuracy of models. • Improved the predictions compared to the baseline using data smoothing methods. • Demonstrated a guide to develop data-driven chiller power and COP models. In recent years, data-driven models have enabled accurate prediction of chiller power consumption and chiller coefficient of performance (COP). This study evaluates the usage of time series Extreme Gradient Boosting (XGBoost) models to predict chiller power consumption and chiller COP of a water-cooled chiller plant. The 10-second measured data used in this study are from the Intelligent Building Agents Laboratory (IBAL), which includes two water-cooled chillers. Preprocessing, data selection, noise analysis, and data smoothing methods influence the accuracy of these data-driven predictions. The data intervals were changed to 30 s, 60 s, and 180 s using down-sampling and averaging strategies to investigate the impact of data preprocessing methods and data resolutions on the accuracy of chiller COP and power consumption models. To overcome the effect of noise on the accuracy of the models of chiller power consumption and COP, two data smoothing methods, the moving average window strategy and the Savitzky-Golay (SG) filter, are applied. The results show that both methods improve the predictions compared to the baseline, with the SG filter slightly outperforming the moving average. Particularly, the mean absolute percentage error of the chiller COP and power consumption models improved from 4.8 to 4.9 for the baseline to 1.9 and 2.3 with the SG filter, respectively. Overall, this study provides a practical guide to developing XGBoost data-driven chiller power consumption and COP prediction models. [ABSTRACT FROM AUTHOR]

Saudi Arabia faces significant challenges in managing the rising energy consumption in buildings driven largely by its hot climatic conditions. As a result, retrofitting building facades to enhance energy efficiency has become a critical strategy. This study assesses the effectiveness of various façade retrofit strategies in reducing cooling electricity consumption using a real-time case study in Dhahran, Saudi Arabia. The strategies explored include external wall upgrades, window replacements, and installation of shading devices. Each strategy was evaluated individually, considering the reduction in heat gains, cooling load, and payback period as key performance indicators. To further maximize energy efficiency, these strategies were also analyzed in combination using the genetic algorithm optimization method, yielding 224 possible facade configurations. The optimal solution included the use of an External Thermal Insulation Composite System (ETCIS) in walls, louvers in windows, and low-emissivity coating with Argon gas-filled glazing, achieving a cooling energy reduction of approximately 16% and a payback period of 14.8 years. This study provides several recommendations for improving the efficiency of retrofitting building façades in hot climatic conditions. [ABSTRACT FROM AUTHOR]

The coal mining industry in Pakistan faces recurring fatal accidents due to data scarcity, lack of research, and technology adoption. This paper reviews the current status of coal mining, the ongoing energy crisis, the utilization of indigenous coal resources, and the prevailing safety challenges. By comparing coal mining safety standards in Pakistan with global benchmarks, this study proposes advanced mining technologies to improve productivity and safety. This study emphasizes the importance of investigating the underlying factors causing mining accidents in order to devise effective strategies to mitigate them. The lack of relevant data and the reluctance to adopt technology in the industry are identified as major obstacles to improving safety conditions. The proposed strategies for overcoming safety issues include improving data collection and analysis, increasing research efforts, and promoting the adoption of advanced technology. Thus, this paper highlights the urgent need to address safety concerns in Pakistan's coal mining industry to avoid further loss of lives and resources. [ABSTRACT FROM AUTHOR]

This research paper presents a detailed analysis of a lightweight block cipher's (LWBC) power consumption and security features, specifically designed for IoT applications. To accurately measure energy consumption during the execution of the LWBC algorithm, we utilised the Qoitech Otii Arc, a specialised tool for optimising energy usage. Our experimental setup involved using the Otii Arc as a power source for an Arduino NodeMCU V3, running the LWBC security algorithm. Our methodology focused on energy consumption analysis using the shunt resistor technique. Our findings reveal that the LWBC is highly efficient and provides an effective solution for energy-limited IoT devices. We also conducted a comparative analysis of the proposed cipher against established LWBCs, which demonstrated its superior performance in terms of energy consumption per bit. The proposed LWBC was evaluated based on various key dimensions such as power efficiency, key and block size, rounds, cipher architecture, gate area, ROM, latency, and throughput. The results of our analysis indicate that the proposed LWBC is a promising cryptographic solution for energy-conscious and resource-limited IoT applications. [ABSTRACT FROM AUTHOR]

The growing adoption of smart meters enables the measurement of households' energy consumption, influenced not solely by building characteristics such as thermal insulation but also by residents' behavioural patterns, such as heating and ventilation practices. To motivate residents to adopt more sustainable behaviours, user interfaces on smartphones and laptops are increasingly using consumption data from households' smart meters to enable effective goal‐setting. In contrast to previous research largely focusing on goal‐setting in isolation, this study examines the role of specific social comparison‐related design features that future research and practitioners can consider along with goal‐setting to stimulate sustainable behaviours. Specifically, we look into the influence of residents' perception of their relative performance (i.e., whether their behaviour was better or worse than a reference group) on their ambition to act (i.e., targeted improvement goal) and their actual energy consumption behaviour. Moreover, we investigate the influence of a goal's evaluative standard (i.e., whether the goal refers to one's own or other's performance) on the relationship between relative performance, ambition to act, and energy consumption behaviour. Drawing on social comparison theory, we conducted a framed field experiment with 152 households. We find that a goal's evaluative standard influences residents' awareness of their relative performance, affecting their ambition to act and, ultimately, their energy consumption behaviour. More specifically, we find that whereas other‐ (vs. self‐) referencing goals encourage residents from worse‐than‐average performing households more strongly to improve their energy consumption behaviour, they discourage better‐than‐average ones. Overall, our study provides novel insights into the interplay between relative performance and evaluative standards as a means of fostering social comparison in smart meter‐facilitated goal‐setting, highlighting their crucial role in effectively supporting sustainable behaviours. [ABSTRACT FROM AUTHOR]

In forest transportation in Quebec, transportation rates are typically based on an estimated trip or travel time duration during which a truck travels loaded from an origin to a destination and then returns empty to the origin. These transportation rates implicitly consider fuel consumption for fuel surcharge costs in negotiation, but for most shippers and carriers, fuel consumption remains a rough estimate, leading to underestimation or overestimation of actual consumption. In this paper, we propose a new fuel consumption model and a more detailed trip time computation to support accurate estimations. The model takes into account road network characteristics that affect fuel consumption, such as road elevation profiles, including slopes that significantly affect fuel consumption compared to flat roads, and curves where trucks change speed, as well as intersections where trucks need to stop or slow down. The road network of the province of Quebec (Canada) is represented in a route network that integrates all the characteristics considered by the fuel consumption model. The model is validated in a case study using a timber truck equipped with GPS and information about overall fuel consumption between a set of refueling points. Utilizing the fuel consumption model and a route planner enables accurate estimation of fuel consumption and, consequently, associated greenhouse gas (GHG) emissions and travel times. A case study involving three companies is then conducted to analyze how more detailed information can inform new transportation rates. [ABSTRACT FROM AUTHOR]

With the rapid development of 5G base station construction, significant energy storage is installed to ensure stable communication. However, these storage resources often remain idle, leading to inefficiency. To enhance the utilization of base station energy storage (BSES), this paper proposes a co-regulation method for distribution network (DN) voltage control, enabling BSES participation in grid interactions. In this paper, firstly, an energy consumption prediction model based on long and short-term memory neural network (LSTM) is established to accurately predict the daily load changes of base stations. Secondly, a BSES aggregation model is constructed by using the power feasible domain maximal inner approximation method and Minkowski summation to evaluate the charging and discharging potential and adjustable capacity of BSES clusters. Subsequently, a BSES demand assessment and optimal scheduling model for low voltage regulation in DN is developed. This model optimizes the charging and discharging strategies of BSES to alleviate low voltage problems in DN. Finally, the simulation results effectively verify the feasibility of the proposed optimal scheduling method of BSES for voltage regulation in DN. [ABSTRACT FROM AUTHOR]

To determine the relationship between the ice amount of packing for transportation of pasteurized milk and the temperature in the foam box and the transportation time under a certain external temperature, and to obtain the appropriate ice amount data. Considering the influencing factors of foam box ice aviation logistics and the actual operation mode, the energy consumption model of foam box ice transportation was established, and the energy consumption balance process was analyzed. On this basis, the ice amount of foam box ice transportation was set as different standards, and the corresponding relationship between the logistics duration and the temperature in the foam box was studied under different external temperatures and 50%~20% ice amount. The relationship curve between the ice proportion and the low temperature time in the foam box, the arrival time of the lowest temperature in the box, and the transportation time under the appropriate temperature was made. Research suggests that the ice amount of 50% can meet all the usual external temperatures within 60 hours of transportation quality. The logistics duration is less than or equal to 24 hours, the recommended ice amount is about 20%. The logistics duration is about 36 hours, the recommended ice amount is about 30%. The logistics duration is about 48 hours, the recommended ice amount is not more than 40%. [ABSTRACT FROM AUTHOR]

In order to overcome the problems of electric vehicle (EV) range anxiety and inadequate charging station, a driver assistance technology is presented here. The proposed technology assists a driver to maintain an efficient driving strategy (EDS) that minimizes energy consumption toward increasing the driving range. Additionally, it provides an anticipated range of EV corresponding to its current battery charge which alerts the driver to take a decision for required charging. EDS is determined by solving a multi-objective optimization problem corresponding to the current route characteristics. Optimization problem considers three primary objectives, minimization of energy consumption and travel time and maximize journey comfort, and follows an EV drive model. Effectiveness of the proposed technology is identified by analyzing the effect of adopting EDS in a micro-trip planning with an E-bus based on experimental drive data. A drive model for the E-bus is formulated and validated through comparative analysis of speed, motor current, and voltage that are obtained in simulation and experimentation. Such analysis was performed independently in four micro-trips identified from E-bus drive data following a typical route. It was found that the driver assistance technology reduces both energy consumption and travel time than that found by following a random driving strategy. [ABSTRACT FROM AUTHOR]

This study is a significant endeavor involving the development and testing of a comprehensive methodology to incorporate driving behavior into the analysis and prediction of vehicle fuel consumption. It underscores the crucial importance of understanding how different driving behavior affect fuel efficiency. The framework we present is a theoretical construct and a practical tool. It provides a robust, multi-step process for linking driving behavior to fuel consumption, leveraging both traditional statistical methods and advanced machine learning techniques to derive actionable insights. To test the framework, we used a naturalistic data that includes about 5408 different road users in a mixed traffic environment and urban settings in Germany. We applied a microscopic fuel consumption model to calibrate the framework and an unsupervised clustering algorithm to classify the behavior of the driver interacting with each other and with vulnerable road users. The framework includes developing Linear regression model as a baseline, which yields an R-squared of 0.511 and a Mean Squared Error (MSE) of 0.031, indicating moderate predictive accuracy. The final step includes choosing Random Forest as a better model, which achieves a higher R-squared of 0.956 and a lower MSE of 0.003. We also found that conservative and aggressive driving leads to significantly higher and more discrepancy in fuel consumption than normal driving behavior. These insights can promote more efficient driving practices, leading to significant fuel savings and environmental benefits. Article highlights: Driving behavior significantly impacts fuel consumption; smooth, controlled driving leads to greater fuel efficiency. Insights from this study can inform ADAS development, enabling real-time, fuel-efficient driving assistance systems. Promoting normal driving habits can reduce fuel use, aiding eco-friendly driving and sustainable transport solutions. [ABSTRACT FROM AUTHOR]

This study introduces an advanced mathematical methodology for predicting energy generation and consumption based on temperature variations in regions with diverse climatic conditions and increasing energy demands. Using a comprehensive dataset of monthly energy production, consumption, and temperature readings spanning ten years (2010–2020), we applied polynomial, sinusoidal, and hybrid modeling techniques to capture the non-linear and cyclical relationships between temperature and energy metrics. The hybrid model, which combines sinusoidal and polynomial functions, achieved an accuracy of 79.15% in estimating energy consumption using temperature as a predictor variable. This model effectively captures the seasonal and non-linear consumption patterns, demonstrating a significant improvement over conventional models. In contrast, the polynomial model for energy production, while yielding partial accuracy (R² = 0.65), highlights the need for more advanced techniques to fully capture the temperature-dependent nature of energy production. The results indicate that temperature variations significantly affect energy consumption, with higher temperatures driving increased energy demand for cooling, while lower temperatures affect production efficiency, particularly in systems like hydropower. These findings underscore the necessity for integrating sophisticated models into energy planning to ensure resilience in energy systems amidst climate variability. The study offers critical insights for policymakers to optimize energy generation and distribution in response to changing climatic conditions. [ABSTRACT FROM AUTHOR]

The electricity consumption of the textile industry accounts for 2.12% of the total electricity consumption in society, making it one of the high-energy-consuming industries in China. The textile industry requires the use of a large amount of industrial steam at various temperatures during production processes, making its dispatch and operation more complex compared to conventional electricity–heat integrated energy systems. As an important demand-side management platform connecting the grid with distributed resources, a virtual power plant can aggregate textile industry users through an operator, regulating their energy consumption behavior and enhancing demand-side management efficiency. To effectively address the challenges in load regulation for textile industry users, this paper proposes a coordinated optimization dispatching method for electricity–steam virtual-based power plants focused on textile industrial parks. On one hand, targeting the impact of different energy prices on the energy usage behavior of textile industry users, an optimization dispatching model is established where the upper level consists of virtual power plant operators setting energy prices, and the lower level involves multiple textile industry users adjusting their purchase and sale strategies and changing their own energy usage behaviors accordingly. On the other hand, taking into account the energy consumption characteristics of steam, it is possible to optimize the production and storage behaviors of textile industry users during off-peak electricity periods in the power market. Through this electricity–steam optimization dispatching model, the virtual power plant operator's revenue is maximized while the operating costs for textile industry users are minimized. Case study analyses demonstrate that this strategy can effectively enhance the overall economic benefits of the virtual power plant. [ABSTRACT FROM AUTHOR]

Optimizing domestic energy use and increasing the efficiency of residential power supply chains depend much on home energy management (HEM) systems. This paper examines the utilization of advanced meta-heuristics, namely the Siberian Tiger Optimization (STO) and Sand Cat Swarm Optimization (SCSO) algorithms, for developing HEM systems. The paper presents an integrated STSC algorithm, enhanced by artificial intelligence, that monitors and optimizes household energy usage. To optimize electricity distribution, this algorithm seeks a compromise between reducing costs and lowering the peak-to-average proportion of power. After extensive simulations, the STSC algorithm outperforms previous meta-heuristics in Peak Average Ratio. It demonstrates the possibility of substantial cost reductions in residential settings, reaching up to 8.5%. This improves the overall efficiency of households' power supply chains. In addition to reducing costs, the STSC algorithm contributes to sustainability objectives by utilizing AI to minimize carbon emissions, including renewable energy sources, and facilitate adaptable demand solutions. This highlights its role in promoting sustainable supply chain practices in energy efficiency. The combined use of STO and SCSO algorithms in the STSC method is a new and innovative development in HEM systems. This study highlights the capacity of AI-driven technologies to effectively and environmentally optimize household energy consumption. [ABSTRACT FROM AUTHOR]

With the rapid development of 5G base station construction, significant energy storage is installed to ensure stable communication. However, these storage resources often remain idle, leading to inefficiency. To enhance the utilization of base station energy storage (BSES), this paper proposes a co-regulation method for distribution network (DN) voltage control, enabling BSES participation in grid interactions. In this paper, firstly, an energy consumption prediction model based on long and short-term memory neural network (LSTM) is established to accurately predict the daily load changes of base stations. Secondly, a BSES aggregation model is constructed by using the power feasible domain maximal inner approximation method and Minkowski summation to evaluate the charging and discharging potential and adjustable capacity of BSES clusters. Subsequently, a BSES demand assessment and optimal scheduling model for low voltage regulation in DN is developed. This model optimizes the charging and discharging strategies of BSES to alleviate low voltage problems in DN. Finally, the simulation results effectively verify the feasibility of the proposed optimal scheduling method of BSES for voltage regulation in DN. [ABSTRACT FROM AUTHOR]

With the development of new technology for multiple energy applications, household appliances using electricity and other energy sources are becoming increasingly abundant. Users can freely choose the way of energy consumption based on the price of electricity and other energy sources. The phenomenon has become one of the important factors affecting real‐time pricing. This study considers real‐time pricing in the case of multiple energy coexistence on the user side. According to the spatiotemporal coupling of electricity and other energy sources and the relationship between other energy sources and total energy consumption, an optimization model that satisfies the actual situation is established. By using the relaxation theory and dual decomposition technology, a partially distributed algorithm is proposed for multi‐period coupling and finding the optimal energy scheduling scheme for the energy company and each user, so as to maximize social welfare. In addition, under the background of multiple energy coupling, real‐time pricing can guide users to adjust their electricity consumption behaviour vertically and horizontally, change the type of energy, meet energy demand for end users and effectively reduce the peak‐to‐average ratio. Simulation results verify the rationality of the model and the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

This study examines the shares of sectors (agriculture, services, industry, transportation and other sectors) in total energy consumption in OECD countries for the period 2011-2020 using Louvain cluster analysis. Energy consumption is an important development indicator and provides important information about the development of countries. In particular, the analysis of the shares of energy consumption of main sectors such as agriculture, services, industry and transport sectors can provide important information about a country's economic diversity, level of industrialization and economic focus. Cluster analysis can provide important insights by identifying countries with similar energy consumption patterns. Louvain cluster analysis was preferred in this study. Louvain clustering has the advantage of being fast and dealing with noise compared to K-means and Hierarchical clustering methods. The results of the study are evaluated from two perspectives. The first one is the inferences obtained from the descriptive statistics of the data set and the second one is the inferences obtained from the clustering analysis. The results of the cluster analysis emphasize the insights offered by the cluster changes in the temporal dimension and the formation of year-based clusters. In addition, the insights provided by the clustering results for Türkiye are evaluated. [ABSTRACT FROM AUTHOR]

The time‐varying external environment is one of the main variables influencing heating energy consumptions, so that its influence should be rectified when energy savings of different heating modes are calculated. This paper proposes an energy consumption rectification method based on Bayesian linear regression and heating degree‐days, to obtain heating energy consumptions without the influence of different outdoor temperatures. The proposed method consists of three main steps. First, a physical model of heating houses is used to prove a relationship between energy consumptions and heating degree‐days. Second, Bayesian linear regression is exploited to estimate uncertainty ranges of heating energy consumptions. Finally, heating energy consumptions are rectified, and energy savings with their uncertainty ranges for different heating modes under the same outdoor temperature are obtained. The proposed method does not require the physical parameters of heating houses to facilitate practical implementation. Additionally, it provides uncertainty ranges of heating energy consumptions to measure the estimation accuracy. Numerical and experimental examples show that the proposed method provides more accurate estimates of heating energy consumptions than existing methods. [ABSTRACT FROM AUTHOR]

Detection of abnormal power consumption is very important to reduce irregular power consumption and economic losses. To reduce power wastage, it is necessary to understand the power consumption habits of users and detect unusual usage behavior promptly. Researchers have developed various methods to detect abnormal electricity consumption behavior. However, it does not provide enough consideration to identify the relationships between different time stamps and multi-scale characteristics of the power utilization series. Therefore, a grey relation projection–based better clustering learning technique is proposed for assessing power consumption abnormalities. Power data from a power consumption information gathering system is analyzed with a diagnosis model for anomalous electricity use by customers constructed using big data techniques and machine learning-related algorithms. Density-based clustering is offered as a means of analyzing instances of excessive electricity use. First, consumers in the same area are grouped based on the similarities between their electricity usage patterns. Then, using density clustering, we identify the most extreme cases of electricity use amongst consumers who are otherwise comparable. Using a comparable user electricity consumption framework and the historical electricity usage approach, we can determine the degree to which these anomalies match the norm. Finally, the threshold value allows for discrimination of anomalous electricity usage with the level of comprehensive support. The proposed DBSCAN approach attains the PR-AUC values of 0.96, and AUC attains 0.911, results from simulations demonstrate that this approach can detect aberrant patterns of electricity consumption. [ABSTRACT FROM AUTHOR]

This study analyses data from natural gas combination boilers across a 6-year timeframe, exploring how demand for space heating and hot water have both changed over time, and highlights the impact of factors such as external temperature and the UK energy price cap. The results show that there has been a significant decrease in annual space heating and hot water demand since 2021. Space heating typically contributes 88% of the total annual gas demand of the boilers, with hot water contributing the other 12%. For the same mean temperature across the fourth quarter (8.5 °C), 2018 had a daily mean energy demand of 50.4 kWh, whereas the 2022 value was 41.4 kWh. This 9.0 kWh (18%) difference of the daily mean for Q4 suggests a shift in consumer demand influenced by other factors such as the energy price cap. This analysis provides additional understanding of how consumer energy demand for heating continues to evolve and invites further studies to be completed on future trends of energy demand for both space heating and hot water. Here, we also highlight the benefit of considering space heating and hot water as separate demands, as this provides additional insights and is something the paper helps to advocate for. [ABSTRACT FROM AUTHOR]

One of the ways to improve energy efficiency in transportation is through efforts aimed at increasing the usage of public transportation by residents. This, in turn, is closely related to residents' preferences. One of the most frequently cited factors influencing the attractiveness and quality of public transportation is the frequency of connections. This is important not only for urban transport but also regional transport, which has significantly lower passenger flows. This paper aims to present how the frequency of connections affects the attractiveness of regional transport. An original method for determining the attractiveness of public transport, based on the share of adult, senior, and youth passengers with single tickets and monthly passes, is introduced. The results of research on the structure of passengers and flows to/from the district center are presented. Based on the research results, attractiveness factors are calculated for each town. The statistical analysis clearly indicates there is a strong correlation between the attractiveness of regional transport and the frequency of connections (Rs = 0.807, p = 0.001). Moreover, for every connection increase, the number of adult passengers will increase by an average of 1.5. Assuming that these additional passengers switch from individual to public transportation, the resulting reduction in energy consumption due to an increase of one connection is 0.33–0.69 kWh for each kilometer traveled by these passengers. [ABSTRACT FROM AUTHOR]

Household energy consumption represents a significant share of global energy usage, highlighting the importance of understanding the factors that influence energy use and identifying potential strategies for conservation. The Caribbean region faces unique challenges in energy sustainability, driven by its heavy dependence on fossil fuels and rising energy demand. The primary aim of this study is to evaluate the current levels of energy awareness and energy consumption among households on the tropical Caribbean island of Curaçao and to determine practical energy-saving opportunities that can significantly reduce both energy consumption and costs. This paper is one of the first to evaluate energy awareness, energy use, and energy-saving opportunities among households in the Caribbean. The study included a literature review of key theories, concepts, and energy-saving strategies, along with a telephone survey of 382 households in Curaçao to examine household energy use, the factors shaping energy behavior, and the connections between energy consumption, behavior, and household income. The main findings of this study reveal that energy-efficient appliances are predominantly used in high-income households, with much lower adoption rates in middle- and low-income households. Cost savings, rather than environmental concerns, emerge as the primary motivation behind energy-saving behavior. Notably, the study highlights that most households in Curaçao are largely unaware of the full range of energy-efficient options available to them for reducing energy consumption. Based on the field study results, several recommendations are offered to enhance energy awareness, expand energy-saving opportunities, and ultimately reduce energy usage. [ABSTRACT FROM AUTHOR]

To improve the performance of Peer-to-Peer (P2P) networks and reduce energy consumption while minimizing "free-riding" behavior, a queuing model is utilized to abstract dynamic changes of peers in hybrid P2P networks. Requesting peers are categorized differently and a discrete-time Geo/Geo/1 + 1 queuing model is established with simultaneous multiple working vacations, waiting threshold N, and two types of servers with different service rates. The steady-state distribution of the system is obtained by using the matrix-geometric solution method, and performance measures such as average queue length, average delay, and total energy consumption of the two types of requesting peers are calculated. Numerical experiments are conducted to analyze the effects of different parameters on the performance measures. Finally, the optimal strategies and Nash equilibrium points of requesting peers are studied, which provide a theoretical basis to suppress "free-riding" behavior. [ABSTRACT FROM AUTHOR]

Energy efficiency in the building has two dimensions; efficient use of energy resources and energy conservation. Energy-efficient houses play a critical role in energy conservation and achieving sustainable built environment goals. The energy efficiency of a house depends on its building envelope (design, orientation, and energy properties of construction material) and operational characteristics (appliance efficiency, energy consumption patterns). Rather than merely ensuring new houses are energy-efficient, it is vital to energy retrofit existing houses. Energy audits can assist in evaluating the energy efficiency of existing houses and identify improvement measures. The main objectives of this study were to identify efficiency improvement opportunities through an energy audit of houses and apartments in Karachi. A detailed energy audit checklist was prepared using literature, standards, expert review, and code requirements. Data was collected through physical and owner-assisted virtual audits of 58 houses in six districts of Karachi. Architectural plans, information about doors, windows, air ducts, thermal images, historical data, and material properties were collected. Audit tools included; an anemometer and thermal imagining camera during physical audits. Android applications were used during owner-assisted audits. Data were analyzed for developing descriptive graphs, U and R-values, Pareto charts, and district-wise mapping of energy properties of the material. As a result, energy improvement opportunities were proposed. These are focused on building envelope aspects, including; window orientation, minimizing air infiltration, and using heat-resistant paint. Improvement measures were confirmed through thermal imaging of selected buildings. [ABSTRACT FROM AUTHOR]

This study aimed to examine the variables that might affect the ecological footprint by using 1992–2015 data of the BRICS countries (Brazil, Russia, India, China, and South Africa). The common correlated effects' mean group test was employed under the assumption of cross-sectional dependence and heterogeneity. In Brazil, an increase in energy consumption, technological development, and globalization decreased the ecological footprint by 0.043, 0.031, and 0.050 units, respectively, while an increase in urbanization increased the ecological footprint by 0.716 units. In Russia, on the other hand, no interpretation could be made because the coefficients were meaningless. In India, it had been observed that an increase of one unit in urbanization reduced the ecological footprint by 0.269 units. In China, it had been determined that a 1-unit increase in energy consumption reduced the ecological footprint by 0.043 units. In South Africa, the results could not be interpreted because the coefficients were insignificant. The results obtained vary according to the countries that revealed that local policies can also be effective on these variables. Therefore, countries have to act by examining the structures on their own to implement the policies they needed. [ABSTRACT FROM AUTHOR]

The significant increase in building energy consumption poses a major challenge to environmental sustainability. In this process, urban morphology plays a pivotal role in shaping building energy consumption. However, its impact may exhibit latent heterogeneity due to differences in temporal resolution and spatial scales. For urban energy planning and energy consumption modeling, it is crucial to pinpoint when and where urban morphology parameters matter, an overlooked aspect in prior research. This study quantitatively explores this heterogeneity, utilizing a detailed dataset from old residential buildings within a university campus. Spatial lag models were employed for cross-modeling across various temporal and spatial dimensions. The results show that annual and seasonal spatial regression models perform best within a 150 m buffer zone. However, not all significant indicators fall within this range, suggesting that blindly applying the same range to all indicators may lead to inaccurate conclusions. Moreover, significant urban morphology indicators vary in quantity, category, and directionality. The green space ratio exhibits correlations with energy consumption in annual, summer, and winter periods within buffer zones of 150 m, 50~100 m, and 100 m, respectively. It notably displays a negative correlation with annual energy consumption but a positive correlation with winter energy consumption. To address this heterogeneity, this study proposes a three-tiered framework—macro-level project decomposition, establishing a key indicator library, and energy consumption comparisons, facilitating more targeted urban energy model and energy management decisions. [ABSTRACT FROM AUTHOR]

Perovskite solar cells (PSCs) have attracted widespread attention due to their low cost and high efficiency. So far, a variety of single‐junction PSCs have been successfully developed and considered for commercialization, including normal PSCs (N‐PSCs), inverted PSCs (I‐PSCs), and carbon‐based PSCs (C‐PSCs) without hole transporter. Herein, the material cost, equipment depreciation cost, and energy consumption of these three types of PSCs (1 m2) in detail are analyzed. As indicated, the total fabrication cost of the N‐PSCs ($86.49) and I‐PSCs ($81.31) is very close, but is significantly reduced to $41.16 for the C‐PSCs (49%–52% reduction) because carbon electrode is much cheaper than noble metal electrode and organic hole transporter. Besides, only a low‐cost slot‐die coating process with low energy consumption is needed for the deposition of carbon electrode, while the expensive physical vapor deposition and reactive plasma deposition processes with high energy consumption are needed for the deposition of the noble metal electrode and organic hole transporter. [ABSTRACT FROM AUTHOR]

The construction sector is a significant contributor to energy consumption and emissions. With the steady increase in the cost of energy carriers and the costs of energy production, the cost for consumers is also increasing. Therefore, the search for solutions capable of reducing energy consumption by increasing the energy efficiency of building structures, in particular the use of prefabricated timber-frame technology, is ongoing. Recent energy supply uncertainties and high costs necessitate the pursuit of green solutions. Timber construction, especially prefabricated timber-frame technology, holds promise due to its renewability and energy efficiency. However, housing estates built using this technology often lack service infrastructure, like shops, crèches, kindergartens, and offices, affecting resident comfort. This study proposes a methodology to select the optimal utility function for a residential building in such an estate, thus enhancing living conditions. The building's potential new functions—a shop, nursery, or office—were evaluated based on economic criteria, thermal comfort, building airtightness, energy efficiency, and vibration comfort. The analysis indicates that converting the building into a shop requires the least capital investment, making it the most economically beneficial option. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic made people reevaluate their energy consumption and energy efficiency. It held up a mirror to humanity's opportunistic and ruthless deployment of energy sources. As the city streets went empty and the international air traffic stopped altogether, people witnessed the significant drop in CO2 emissions, realized their dependence upon energy sources, and became aware of global climate change. In addition, with lockdowns and home offices, the demand for energy and the usual peak hours shifted offering a new perspective in energy consumption. The pandemic delivered a devastating blow to the human belief in having the constant and everlasting supply of energy that can be relied upon in everything. All these attracted the attention of researchers focusing on energy consumption and efficiency during the COVID‐19 pandemic. As a result, the vast body of academic literature on the topic burgeoned in the past four years yielding many exciting perspectives and viewpoints. Our paper conducts a comprehensive review of this body of literature employing the bibliometric network analysis of 12960 publications indexed in Web of Science database. It demonstrates the potential benefits and challenges associated with implementing energy‐saving technologies, altering energy consumption behavior, and implementing novel ICT solutions in a post‐COVID world. In addition, it highlights the importance of energy efficiency measures and examines novel technologies that can contribute to a sustainable and resilient energy future. Our findings emphasize the need for robust policies, technological advancements, and public engagement for fostering energy efficiency and mitigating environmental impacts of post‐pandemic energy consumption. [ABSTRACT FROM AUTHOR]

This study investigates the optimization of a low-cost IoT-based weather station designed for disaster monitoring, focusing on minimizing power consumption. The system architecture includes application, middleware, communication, and sensor layers, with solar power as the primary energy source. A novel task scheduling algorithm was developed to reduce power usage by efficiently managing the sensing and data transmission periods. Experiments compared the energy consumption of polling and deep sleep techniques, revealing that deep sleep is more energy-efficient (4.73% at 15 s time intervals and 16.45% at 150 s time intervals). Current consumption was analyzed across different test scenarios, confirming that efficient task scheduling significantly reduces power consumption. The energy consumption models were developed to quantify power usage during the sensing and transmission phases. This study concludes that the proposed system, utilizing affordable hardware and solar power, is an effective and sustainable solution for disaster monitoring. Despite using non-low-power devices, the results demonstrate the importance of adaptive task scheduling in extending the operational life of IoT devices. Future work will focus on implementing dynamic scheduling and low-power routing algorithms to enhance system functionality in resource-constrained environments. [ABSTRACT FROM AUTHOR]

Manufacturing plays a vital role in fostering economic growth, yet it is one of the major consumers of energy. Considering energy consumption when devising an Aggregate Production Plan (APP) is a determinant of a more energy-efficient production schedule at the operational level. Manufacturers in emerging economies envisage planning challenges when energy reforms are introduced. A guide for analysis and decision-making in the transitional phase of phasing out energy subsidies is essential. This work tackles this problem by developing two mixed-integer linear programming models for energy-aware production planning. The first model targets maximising profit while explicitly using the energy cost as one of the cost elements. The second is an energy-driven model to minimise energy consumption while including profit as a constraint. The proposed models were validated via a real-life dataset. The models provide the decision-makers with the flexibility to improve their production plans' energy efficiency by either developing plans that reduce energy consumption without sacrificing profit or developing production plans that increase profit without increasing energy consumption. The results imply that adopting Energy-Efficient Production Planning (EEPP) is substantial with high subsidy levels. For low subsidy levels, strategic decision-making entailing the deployment of technological solutions is more efficient. [ABSTRACT FROM AUTHOR]

Residential electricity consumption and time spent at home by household members increased while household income decreased during the COVID-19 pandemic restrictions. Using survey data of Japanese households purchasing electricity from the Tokyo Electric Power Company Holdings, before and during the pandemic, we examine the various dynamics at play involving income, increased time spent at home by both partners and the role of genders in energy consumption. Results show a positive relationship between changes in electricity consumption and changes in household income, suggesting that households reduced their electricity usage following a decrease in income. Interestingly, the results also show that consumption changes are positively correlated to changes in hours spent at home by working husbands but negatively correlated to changes in the hours spent at home by working wives. [ABSTRACT FROM AUTHOR]

Human behavior is one of the reasons for the discrepancy between the actual operational and designed energy consumption of a building. A passive ultra-low-energy residential building in Harbin was used as a case study to explore the relationship between indoor heating temperature settings, window-opening behavior of occupants, and building energy consumption. The field test results revealed that the indoor temperature of the passive house exceeded the upper limit of the winter comfortable indoor temperature range 78.7% of the time during the test period. Window-opening rates for winter are low but vary significantly with time, with the average value on weekends being 2.5 times higher than that on weekdays. The simulation results showed that the high heating temperature and window-opening behavior of occupants mainly contributed to the heating energy consumption exceeding the design standard in the actual operation of this passive house. For every 1 °C increase in indoor heating temperature, heating energy consumption increased by 5.2%. Heating energy consumption increased by 5.6% for every additional 5 min of window opening. The results highlight the influence of occupant adaptive behavior on building energy consumption, which could guide occupants to use energy reasonably. [ABSTRACT FROM AUTHOR]

The residential sector is a critical contributor to global energy consumption. In Ghana, it accounts for 47% of the total electricity consumed. However, inadequate knowledge of categories of residential consumers, their profiles, and the implications on their consumption patterns could impede the development of targeted and sustainable interventions. This study examined the behavioral patterns among low-income urban households in Kumasi, using their socio-demographic and housing characteristics, and self-reported values. This is coherent to the current discourse on Ghana’s transition to zero carbon by 2070 given that Kumasi is the fastest-growing city in the country. Using quantitative methods within a transdisciplinary research design, a survey of 285 households was conducted. The findings yielded varied outcomes. Households reported good electricity use behavior (e.g. covering liquids in the refrigerator) and poor behaviors (e.g. not adjusting the power setting of refrigerators to weather conditions). Logistic regression operations showed associations between energy use behavior, the type of housing units and the type of electricity meters. A significant proportion of respondents exhibited pro-environmental behaviors across all nine value domains. The study concludes that low-income households engage in energy-saving practices. We suggest that policy mediation could enhance the effectiveness of behavioral interventions toward electricity use efficiency. [ABSTRACT FROM AUTHOR]

Urbanization is growing at a rapid pace, and this is being reflected in the rising energy consumption from fossil fuels, which is contributing significantly to greenhouse gas impacts and carbon emissions (CE). Aiming at the problems of the time delay, inconsistency, uneven spatial coverage scale, and low precision of the current regional carbon emissions from energy consumption accounting statistics, this study builds a precise model for estimating the carbon emissions from regional energy consumption and analyzes the spatio-temporal characteristics. Firstly, in order to estimate the carbon emissions resulting from energy consumption, a fixed effects model was built using data on province energy consumption and NPP-VIIRS-like nighttime lighting data. Secondly, the PRD urban agglomeration was selected as the case study area to estimate the carbon emissions from 2012 to 2020 and predict the carbon emissions from 2021 to 2023. Then, their multi-scale spatial and temporal distribution characteristics were analyzed through trends and hotspots. Lastly, the influence factors of CE from 2012 to 2020 were examined with the OLS, GWR, GTWR, and MGWR models, as well as a ridge regression to enhance the MGWR model. The findings indicate that, from 2012 to 2020, the carbon emissions in the PRD urban agglomeration were characterized by the non-equilibrium feature of "high in the middle and low at both ends"; from 2021 to 2023, the central and eastern regions saw the majority of its high carbon emission areas, the east saw the region with the highest rate of growth, the east and the periphery of the high value area were home to the area of medium values, while the southern, central, and northern regions were home to the low value areas; carbon emissions were positively impacted by population, economics, land area, and energy, and they were negatively impacted by science, technology, and environmental factors. This study could provide technical support for the long-term time-series monitoring and remote sensing inversion of the carbon emissions from energy consumption in large-scale, complex urban agglomerations. [ABSTRACT FROM AUTHOR]

The purpose of this paper was to analyze the possibility of changes in energy intensity of production in the context of farm investment scale. The empirical section relies on unpublished FADN microdata. The study answers the question of whether investments and fixed capital growth can contribute to improvements in environmental performance of agricultural production. As it turns out, the group of farms with the greatest amount of investments saw an increase in energy consumption costs, though at a much slower rate than growth in production value. As a result, there was a drop in energy intensity of production, defined as the ratio between energy costs and production value. These findings corroborate the hypothesis advanced in this study, namely that upon reaching a sufficiently large amount of investments, farms can become capable of reducing the energy intensity of their production activity. Hence, higher levels of capital productivity are attained when the farms invest in more energy-efficient fixed assets. Conclusions cannot be universal, as the analysis involved a portion of selected farms. However, the research indicates a trend in the study of energy intensity of production depending on the scale of investment and the validity of the investment comprehensiveness approach. These conclusions provide a basis for recommendations for an agricultural policy which should include support for investments that are large and comprehensive (in relation to the farm's assets) and go beyond the simple renewal of assets. Therefore, in its investment-oriented measures, the agricultural policy should take the innovativeness (including energy efficiency) criterion into account. [ABSTRACT FROM AUTHOR]

Accurate load forecasting is essential for the efficient and reliable operation of power systems. Traditional models primarily utilize unidirectional data reading, capturing dependencies from past to future. This paper proposes a novel approach that enhances load forecasting accuracy by fine tuning an attention-based model with a bidirectional reading of time-series data. By incorporating both forward and backward temporal dependencies, the model gains a more comprehensive understanding of consumption patterns, leading to improved performance. We present a mathematical framework supporting this approach, demonstrating its potential to reduce forecasting errors and improve robustness. Experimental results on real-world load datasets indicate that our bidirectional model outperforms state-of-the-art conventional unidirectional models, providing a more reliable tool for short and medium-term load forecasting. This research highlights the importance of bidirectional context in time-series forecasting and its practical implications for grid stability, economic efficiency, and resource planning. [ABSTRACT FROM AUTHOR]

The prediction of fuel consumption and Carbon Intensity Index (CII) of ships is crucial for optimizing decarbonization strategies in the maritime industry. This study proposes a ship fuel consumption prediction model based on the Long Short-Term Memory with Self-Attention Mechanism (SA-LSTM). The model is applied to a container ship of 2400 TEU to predict its hourly fuel consumption, hourly CII, and annual CII rating. Four different feature sets are selected from these data sources and are used as inputs for SA-LSTM and another ten models. The results demonstrate that the SA-LSTM model outperforms the other models in prediction accuracy. Specifically, the Mean Absolute Percentage Error (MAPE) for fuel consumption predictions using the SA-LSTM model is reduced by up to 20% compared to the XGBoost and by up to 12% compared to the LSTM model. Additionally, the SA-LSTM model achieves the highest accuracy in annual CII predictions. [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]

Due to the scattered distribution and poor clustering of abnormal clusters in energy big data, the ability to detect anomalies is poor. Therefore, a high-energy data anomaly clustering detection method based on redundant convolutional encoding is proposed. Quantitative analysis of the coupling characteristics of electrical thermal gas optical time series for multi energy users based on Copula function, and incorporating quantitative values into multi energy feature indicators to extract the energy consumption behavior characteristics of multi energy users. Utilize redundant convolutional codecs to recombine and structurally encode abnormal features of energy big data, and capture multi energy coupling time features using coupling time capsule layers. Then, coupling time features are synthesized through fully connected linear regression layers to generate anomalous clustering feature components, and the energy time series data is then transformed into feature values of the time series in three-dimensional space. Based on this, a comprehensive energy system and massive multi energy user energy big data anomaly clustering analysis are carried out to determine the optimal number of multi energy users. Then, based on linear layers, the electricity heat gas light load characteristic map of multi energy users is transformed into one-dimensional form, and an energy big data anomaly clustering detection model is constructed to complete anomaly detection. The simulation results show that the proposed method has excellent feature clustering performance, detection accuracy above 98.7%, fast convergence speed, and an error rate below 0.1, which has reliable application value. [ABSTRACT FROM AUTHOR]

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]

Long-range frequency hopping spread spectrum (LR-FHSS) is a pivotal advancement in the LoRaWAN protocol that is designed to enhance the network's capacity and robustness, particularly in densely populated environments. Although energy consumption is paramount in LoRaWAN-based end devices, this is the first study in the literature, to our knowledge, that models the impact of this novel mechanism on energy consumption. In this article, we provide a comprehensive energy consumption analytical model of LR-FHSS, focusing on three critical metrics: average current consumption, battery lifetime, and energy efficiency of data transmission. The model is based on measurements performed on real hardware in a fully operational LR-FHSS network. While in our evaluation, LR-FHSS can show worse consumption figures than LoRa, we find that with optimal configuration, the battery lifetime of LR-FHSS end devices can reach 2.5 years for a 50 min notification period. For the most energy-efficient payload size, this lifespan can be extended to a theoretical maximum of up to 16 years with a one-day notification interval using a cell-coin battery. [ABSTRACT FROM AUTHOR]

Predicting errors in home appliances is crucial for maintaining the reliability and efficiency of smart homes. However, there is a significant lack of such data on appliance malfunctions that can be used in developing effective anomaly detection models. This research paper presents a novel approach for simulating errors of heterogeneous home appliance power consumption patterns. The proposed model takes normal consumption patterns as input and employs advanced algorithms to produce labeled anomalies, categorizing them based on the severity of malfunctions. One of the main objectives of this research involves developing models that can accurately reproduce anomaly power consumption patterns, highlighting anomalies related to major, minor, and specific malfunctions. The resulting dataset may serve as a valuable resource for training algorithms specifically tailored to detect and diagnose these errors in real-world scenarios. The outcomes of this research contribute significantly to the field of anomaly detection in smart home environments. The simulated datasets facilitate the development of predictive maintenance strategies, allowing for early detection and mitigation of appliance malfunctions. This proactive approach not only improves the reliability and lifespan of home appliances but also enhances energy efficiency, thereby reducing operational costs and environmental impact. [ABSTRACT FROM AUTHOR]

As the EU strives to achieve its climate goals, it is becoming increasingly crucial to understand the complex relationships between economic activity, energy consumption, and carbon emissions. In this context, our paper aims to investigate the correlation between carbon emissions, energy consumption, and economic development. To fulfill our aim, we have used Eurostat and OECD data for the EU-27 member states for a period of 13 years (2010–2022), using a linear regression as the main analysis method. Our results have shown that there is a strong correlation between demand-based and production-based CO2 emissions as well as between production-based CO2 emissions and final energy consumption, while at the same time, our findings have shown that there is no direct correlation between energy consumption and economic development, aligning our study with the neutrality hypothesis of the energy growth nexus. This paper expands the ongoing discussion on sustainable development and climate change mitigation by conducting a thorough analysis of the EU-27 countries over a span of thirteen years. The results emphasize the need for integrated strategies that address both production and consumption emissions, emphasize the vital role of energy efficiency, and raise questions about the effectiveness of increasing energy consumption to enhance economic productivity or CO2 efficiency. [ABSTRACT FROM AUTHOR]