Search Results (567)

Microgrids play a crucial role in the development of future smart grids, with multiple interconnected microgrids forming large-scale multi-microgrid systems that operate as smart grids. Multi-agent system (MAS)-based control solutions are the most suitable for addressing such control challenges. This paper presents a demand-side management (DSM) strategy using a meta-heuristic optimization technique for minimizing the household energy consumption cost using MAS. The binary grey wolf optimization algorithm (BGWOA) optimizes load scheduling, reducing electricity costs, without compromising consumer preferences using time-of-day (ToD) tariffs. The communication agents and load agents comprise the MAS used to streamline load control operations. The results demonstrate that MAS-based load control using metaheuristic optimization techniques enhances demand-side management, thus minimizing the electricity costs while adhering to contradictory parameters like user preferences, appliance duration, and load atomicity. This makes renewable energy integration more cost-effective in smart grids, thereby ensuring affordable, reliable, and sustainable energy for all. Full article

Traditional energy conservation policies have primarily relied on economic incentives and informational campaigns. However, recent insights from behavioral and social sciences indicate that subtle behavioral interventions, particularly social nudges, can significantly influence household electricity use. This paper presents a structured review of 23 recent field studies examining how social nudging strategies, such as peer comparison, group identity, and normative messaging, have contributed to measurable reductions in electricity consumption. By analyzing intervention outcomes across different regions and formats, we identify key success factors, limitations, and policy implications. Special attention is given to ethical considerations, fairness in implementation, and potential challenges in sustaining behavior change. This study offers a framework for integrating social nudges into future energy policies, emphasizing their role as low-cost, scalable tools for promoting sustainable energy behavior. Full article

With the increasing integration of renewable energy and smart technologies in residential energy systems, proactive household energy management (HEM) have become critical for reducing costs, enhancing grid stability, and achieving sustainability goals. This study proposes a ultra-short-term forecasting-driven proactive energy consumption optimization strategy that integrates advanced forecasting models with multi-objective scheduling algorithms. By leveraging deep learning techniques like Graph Attention Network (GAT) architectures, the system predicts ultra-short-term household load profiles with high accuracy, addressing the volatility of residential energy use. Then, based on the predicted data, a comprehensive consideration of electricity costs, user comfort, carbon emission pricing, and grid load balance indicators is undertaken. This study proposes an enhanced mixed-integer optimization algorithm to collaboratively optimize multiple objective functions, thereby refining appliance scheduling, energy storage utilization, and grid interaction. Case studies demonstrate that integrating photovoltaic (PV) power generation forecasting and load forecasting models into a home energy management system, and adjusting the original power usage schedule based on predicted PV output and water heater demand, can effectively reduce electricity costs and carbon emissions without compromising user engagement in optimization. This approach helps promote energy-saving and low-carbon electricity consumption habits among users. Full article

Global building energy consumption is significantly increasing. Utilizing renewable energy sources may be an effective approach to achieving low-carbon and energy-efficient buildings. A combined system incorporating solar photovoltaic–thermal (PV/T) components with an air-source heat pump (ASHP) was studied for simultaneous heating and power generation in a real residential building. The back panel of the PV/T component featured a novel polygonal Freon circulation channel design. A prototype of the combined heating and power supply system was constructed and tested in Fuzhou City, China. The results indicate that the average coefficient of performance (COP) of the system is 4.66 when the ASHP operates independently. When the PV/T component is integrated with the ASHP, the average COP increases to 5.37. On sunny days, the daily average thermal output of 32 PV/T components reaches 24 kW, while the daily average electricity generation is 64 kW·h. On cloudy days, the average daily power generation is 15.6 kW·h; however, the residual power stored in the battery from the previous day could be utilized to ensure the energy demand in the system. Compared to conventional photovoltaic (PV) systems, the overall energy utilization efficiency improves from 5.68% to 17.76%. The hot water temperature stored in the tank can reach 46.8 °C, satisfying typical household hot water requirements. In comparison to standard PV modules, the system achieves an average cooling efficiency of 45.02%. The variation rate of the system’s thermal loss coefficient is relatively low at 5.07%. The optimal water tank capacity for the system is determined to be 450 L. This system demonstrates significant potential for providing efficient combined heat and power supply for buildings, offering considerable economic and environmental benefits, thereby serving as a reference for the future development of low-carbon and energy-saving building technologies. Full article

To support the national goals of carbon peaking and carbon neutrality, this study proposes a household carbon emission prediction model based on Gene Expression Programming (GEP) for low-carbon retrofitting of aging residential areas in arid-cold regions. Focusing on 15 typical aging communities in Kundulun District, Baotou City, a 17-dimensional dataset encompassing building characteristics, demographic structure, and energy consumption patterns was collected through field surveys. Key influencing factors (e.g., electricity usage and heating energy consumption) were selected using Pearson correlation analysis and the Random Forest (RF) algorithm. Subsequently, a hybrid prediction model was constructed, with its parameters optimized by minimizing the root mean square error (RMSE) as the fitness function. Experimental results demonstrated that the model achieved an R² value of 0.81, reducing RMSE by 77.1% compared to conventional GEP models and by 60.4% compared to BP neural networks, while significantly improving stability. By combining data dimensionality reduction with adaptive evolutionary algorithms, this model overcomes the limitations of traditional methods in capturing complex nonlinear relationships. It provides a reliable tool for precision-based low-carbon retrofits in aging residential areas of arid-cold regions and offers a methodological advance for research on building carbon emission prediction driven by urban renewal. Full article

One of the methods for mitigating the duck curve phenomenon in photovoltaic (PV) energy systems is storing surplus energy in the form of hydrogen. However, there is a lack of studies focused on residential PV systems that assess the impact of hydrogen storage on the reduction of energy flow imbalance to and from the national grid. This study presents an analysis of hydrogen energy storage based on real-world data from a household PV installation. Using simulation methods grounded in actual electricity consumption and hourly PV production data, the research identified the storage requirements, including the required operating hours and the capacity of the hydrogen tank. The analysis was based on a 1 kW electrolyzer and a fuel cell, representing the smallest and most basic commercially available units, and included a sensitivity analysis. At the household level—represented by a single-family home with an annual energy consumption and PV production of approximately 4–5 MWh over a two-year period—hydrogen storage enabled the production of 49.8 kg and 44.6 kg of hydrogen in the first and second years, respectively. This corresponded to the use of 3303 kWh of PV-generated electricity and an increase in self-consumption from 30% to 64%. Hydrogen storage helped to smooth out peak energy flows from the PV system, decreasing the imbalance from 5.73 kWh to 4.42 kWh. However, while it greatly improves self-consumption, its capacity to mitigate power flow imbalance further is constrained; substantial improvements would necessitate a much larger electrolyzer proportional in size to the PV system’s output. Full article

In various applications, IoT temporal data play a crucial role in accurately predicting future trends. Traditional models, including Rolling Window, SVR-RBF, and ARIMA, suffer from a potential accuracy decrease because they generally use all available data or the most recent data window during training, which can result in the inclusion of noisy data. To address this critical issue, this paper proposes a new forecasting technique called Adaptive Holt–Winters (AHW). The AHW approach utilizes two models grounded in an exponential smoothing methodology. The first model is trained on the most current data window, whereas the second extracts information from a historical data segment exhibiting patterns most analogous to the present. The outputs of the two models are then combined, demonstrating enhanced prediction precision since the focus is on the relevant data patterns. The effectiveness of the AHW model is evaluated against well-known models (Rolling Window, SVR-RBF, ARIMA, LSTM, CNN, RNN, and Holt–Winters), utilizing various metrics, such as RMSE, MAE, p-value, and time performance. A comprehensive evaluation covers various real-world datasets at different granularities (daily and monthly), including temperature from the National Climatic Data Center (NCDC), humidity and soil moisture measurements from the Basel City environmental system, and global intensity and global reactive power from the Individual Household Electric Power Consumption (IHEPC) dataset. The evaluation results demonstrate that AHW constantly attains higher forecasting accuracy across the tested datasets compared to other models. This indicates the efficacy of AHW in leveraging pertinent data patterns for enhanced predictive precision, offering a robust solution for temporal IoT data forecasting. Full article

Electric vehicles are gaining attention and being adopted by new users every day. Their widespread use creates a new scenario and challenge for the energy system due to the high energy storage demands they generate. Forecasting these loads using artificial neural networks has proven to be an efficient way of solving time series problems. This study employs a multilayer perceptron network with backpropagation training and Bayesian regularisation to enhance generalisation and minimise overfitting errors. The research aggregates real consumption data from 200 households and 348 electric vehicles. The developed method was validated using MAPE, which resulted in errors below 6%. Short-term forecasts were made across the four seasons, predicting the total aggregate demand of households and vehicles for the next 24 h. The methodology produced significant and relevant results for this problem using hybrid training, a few-neuron architecture, deep learning, fast convergence, and low computational cost, with potential for real-world application. The results support the electrical power system by optimising these loads, reducing costs and energy generation, and preparing a new scenario for EV penetration rates. Full article

Rapid industrialization, widespread transportation electrification, and significantly rising household energy consumption are rapidly increasing global electricity demand. Climate change and dependency on fossil fuels to meet this demand underscore the critical need for sustainable energy solutions. Microgrids (MGs) provide practical applications for renewable energy, reducing reliance on fossil fuels and mitigating ecological impacts. However, renewable energy poses reliability challenges due to its intermittency, primarily influenced by weather conditions. Additionally, fluctuations in fuel prices and the management of multiple devices contribute to the increasing complexity of MGs and the necessity to address a range of objectives. These factors make the optimization of Energy Management Strategies (EMSs) essential and necessary. This study contributes to the field by categorizing the main aspects of MGs and optimization EMS, analyzing the impacts of weather on MG performance, and evaluating their effectiveness in handling multi-objective optimization and data considerations. Furthermore, it examines the pros and cons of different methodologies, offering a thorough overview of current trends and recommendations. This study serves as a foundational resource for future research aimed at refining optimization EMS by identifying research gaps, thereby informing researchers, practitioners, and policymakers. Full article

Municipal solid waste management (MSWM) on islands involves several challenges relating to politics, society, the environment, and technology. This paper addresses the potential for producing biogas and biomethane from food waste on Tenerife, including waste from households, with the aim of reducing landfill and primary fossil energy consumption. The study also introduces the European and Regional policy framework and requirements. Effective microorganisms have been studied as proposals to stabilise the food waste from households, avoiding odours and decomposition during storage. The trials show positive results in terms of the preservation of organic matter until the food waste is transported to the biogas plant. In addition, a new concept for a small biogas plant made of textile materials, which are suited to the municipalities of Tenerife, is presented to provide an easy-to-build solution, with ranges of up to 75 kW in electrical power. With a theoretical potential of 299,012 tons of food waste being available per year (based on 2022), preliminary laboratory experiments with real samples of the island showed a theoretical potential of 28.97 × 10⁶ Nm³ for biogas and 264,612 tons for digestate, which can be used as fertilisers, with potential savings of 18.15 × 10⁶ L of gasoline and 42.66 × 10³ equivalent CO₂ tons. Full article

Non-Intrusive Load Monitoring (NILM) technology, enabled by high-precision electrical data acquisition sensors at household entry points, facilitates real-time monitoring of electricity consumption, enhancing user interaction with smart home systems and reducing electrical safety risks. However, the growing diversity of household appliances and limitations in NILM accuracy and robustness necessitate innovative solutions. Additionally, outdated public datasets fail to capture the rapid evolution of modern appliances. To address these challenges, we constructed a high-sampling-rate voltage–current dataset, measuring 15 common household appliances across diverse scenarios in a controlled laboratory environment tailored to regional grid standards (220 V/50 Hz). We propose an AI-driven NILM method that integrates power-mapped, color-coded voltage–current (V–I) trajectories with frequency-domain features to significantly improve load recognition accuracy and robustness. By leveraging deep learning frameworks, this approach enriches temporal feature representation through chromatic mapping of instantaneous power and incorporates frequency-domain spectrograms to capture dynamic load behaviors. A novel channel-wise attention mechanism optimizes multi-dimensional feature fusion, dynamically prioritizing critical information while suppressing noise. Comparative experiments on the custom dataset demonstrate superior performance, particularly in distinguishing appliances with similar load profiles, underscoring the method’s potential for advancing smart home energy management, user-centric energy feedback, and social informatics applications in complex electrical environments. Full article

In the housing sector emission reduction builds on a shift from fossil fuels to renewable energy sources and increasing the efficiency of energy usage, with heating playing a dominant role in comparison to that of electricity. For electricity production in the residential sector, research shows that different settings of (co-)ownership in renewables are linked to a greater tendency to invest in energy-efficient devices or to adopt more energy-conscious behaviours. The empirical analysis demonstrates that fully-fledged prosumers, i.e., consumers who have the option to choose between self-consumption and selling to third parties or the grid, exhibit a higher tendency to invest in energy efficiency and that only this group manifests a greater likelihood of engaging in conscious-energy consumption behaviour. This paper extends the analysis to include heating in the residential sector. The study conducted an ANCOVA based on a sample of 2585 German households. The findings show that, depending on the (co-)ownership setting, the willingness to invest and to adopt energy-efficient practices grows considerably. Consumer-sellers demonstrate the highest willingness to invest and adapt energy conscious behaviour. Furthermore, regarding heating in particular, self-consumers are also inclined to invest and engage in energy-savings behaviour. Full article

The integration of renewable energy into residential microgrids presents significant challenges due to solar generation intermittency and variability in household electricity demand. Traditional forecasting methods, reliant on historical data, fail to adapt effectively in dynamic scenarios, leading to inefficient energy management. This paper introduces a novel adaptive energy management framework that integrates streaming machine learning (SML) with a hierarchical fractal microgrid architecture to deliver precise real-time electricity demand forecasts for a residential community. Leveraging incremental learning capabilities, the proposed model continuously updates, achieving robust predictive performance with mean absolute errors (MAE) across individual households and the community of less than 10% of typical hourly consumption values. Three battery-sizing scenarios are analytically evaluated: centralised battery, uniformly distributed batteries, and a hybrid model of uniformly distributed batteries plus an optimised central battery. Predictive adaptive management significantly reduced cumulative grid usage compared to traditional methods, with a 20% reduction in energy deficit events, and optimised battery cycling frequency extending battery lifecycle. Furthermore, the adaptive framework conceptually aligns with digital twin methodologies, facilitating real-time operational adjustments. The findings provide critical insights into sustainable, decentralised microgrid management, emphasising improved operational efficiency, enhanced battery longevity, reduced grid dependence, and robust renewable energy utilisation. Full article

The Renewable Energy Community (REC) has emerged in Europe, encouraging the use of renewable energy sources (RESs) within localities, bringing social, economic, and environmental benefits. RESs are characterized by various loads, including household consumption, storage systems, and the increasing integration of electric vehicles (EVs). EVs offer opportunities for distributed RESs, such as photovoltaic (PV) systems, which can be economically advantageous for RECs whose members own EVs and charge them within the community. This article focuses on the integration of PV systems and the management of energy loads for different participants—consumers and prosumers—along with a small EV charging setup in the REC. A REC consisting of a multi-unit building is examined through a mathematical and numerical model. In the model, hourly PV generation data are obtained from the PVGIS tool, while residential load data are modeled by converting monthly electricity bills, including peak and off-peak details, into hourly profiles. Finally, EV hourly load data are obtained after converting the data of voltage and current data from the charging monitoring portal into power profiles. These data are then used in our mathematical model to evaluate energy fluxes and to calculate self-consumed, exported, and shared energy within the REC based on energy balance criteria. In the model, an energy management system (EMS) is included within the REC to analyze EV charging behavior and optimize it in order to increase self-consumption and shared energy. Following the EMS, it is also suggested that the number of EVs to be charged should be evaluated in light of energy-sharing incentives. Numerical results have been reported for different seasons, showing the possibility for the owners of EVs to charge their vehicles within the community to optimize self-consumption and shared energy. Full article

This paper examines whether reference-dependent preferences play a role in determining household electricity consumption in the Korean context. To do so, we first establish six variables of reference costs based on monthly electricity billing information of the 1040 Korean household survey dataset and then test whether these reference costs affect the electricity consumption in the subsequent months using a probit regression analysis. The empirical results show that the residential electricity consumption for the current month is determined by the reference cost in comparison to the actual costs of the previous months. The significant role of reference costs in determining electricity consumption implies that the behaviors of the Korean residential electricity consumers can be explained by the prospect theory. Furthermore, as a policy implication, these results suggest non-price interventions for residential electricity conservation in Korea. Full article