Energy Informatics最新文献

With the intensification of the global energy crisis and the increase in environmental awareness, energy-saving problems related to household appliances have garnered widespread attention. Here, the usage patterns of electric washing machine users and their energy-saving potential was mainly explored, so as to improve the current situation that the influencing factors of existing research behaviors were not deep enough and the energy saving potential was not specific enough. A questionnaire survey was used to gather information on 20,840 users, including individual characteristics, energy-saving awareness, and usage behavior. The study analyzed the differences in users’ energy-saving awareness and behavior through a series of analysis methods, and evaluated the energy-saving and water-saving potential of electric washing machines. The results showed that user behavior such as washing mode, washing temperature, and the volume ratio of clothes significantly affected on the energy and water consumption of electric washing machines. Individual characteristics of users such as gender, age, educational background, and family income were strongly correlated with their awareness of and decisions made regarding energy conservation. Improving the energy efficiency of electric washing machines and optimizing user purchasing behavior could result in 38,787.54 GWh national energy savings potential, and 6.90 million tons of water-saving potential. This study will help manufacturers and government departments better understand consumers’ usage behavior regarding electric washing machines, which could allow them to modify their market strategies and bolster the promotion and education of energy efficiency labels for electric washing machines. This also could support the nation’s objectives for environmental preservation, water and energy conservation, and the sale of products with lesser energy efficiency.

As global energy demand continues to rise and renewable energy sources develop rapidly, the operational efficiency and stability of power systems have emerged as primary challenges in energy management. Line loss within these systems is a critical factor for both energy efficiency and economic performance. This study leverages an electric energy data management platform that facilitates the sharing of archival information, the development of line loss calculation models, and the automated computation of electricity and line loss formulas. This ensures accurate and real-time calculations of line losses in the power grid, supporting multi-time scale analyses and providing timely, comprehensive data for effective line loss management. The platform utilizes theoretical line loss values to identify anomalies, which are categorized into five types: topological relationships, archival information, data collection, electricity metering, and consumption behavior. In response to the abnormal monthly power imbalance rate of 220 kV and 110 KV stations, and the − 3.684% exceeding the − 1% assessment limit, the designed line loss management system service layer does not need to go deep into the bottom layer of the power system. It hides the complexity of the power grid through middleware and provides data, application, and security services.

The renovation and evaluation of building energy-saving projects can provide important support for building an energy-saving society. The study proposes using the contract energy management model to analyze building energy-saving projects and construct an evaluation index system. We also innovatively integrated the Analytic Hierarchy Process and Entropy Weight Method to calculate the weights of indicators, in order to leverage the effective influence of subjective and objective factors. Finally, we used Grey Cluster Analysis to obtain the evaluation effect of building energy-saving projects. Through weight calculation and evaluation analysis, it was found that the energy-saving rates of year-end electricity consumption and air conditioning electricity consumption in buildings after energy-saving renovation were 59.80% and 54.95%, respectively. The overall effectiveness of energy-saving buildings was above 50%, indicating a significant energy-saving effect. In the indicator evaluation system, the weight results of energy-saving service company indicators were relatively high, with values of 0.52, 0.48, and 0.51, respectively. The transformation effect was relatively good. The building energy-saving cost and economic benefits obtained from a 65% energy-saving rate were 3 million yuan and 530,000 yuan, respectively, which were significantly better than the simulation results of other energy-saving rates. The contract energy management model based on the fusion weight method and grey clustering method has superiority, which is effective for evaluating building energy-saving projects. It also provides technical reference and scientific suggestions for building energy-saving renovation.

Due to the intermittency and volatility of distributed power sources, the microgrid system has poor stability and high operation cost. Therefore, the study proposes an economic optimization scheduling strategy based on the chaotic mapping butterfly optimization algorithm and the mathematical model of microgrid group system. The study creates simulation trials of function poles and microgrid group operation to confirm the strategy’s efficacy. According to the experimental findings, the multimodal function of the enhanced butterfly optimization method had a variance of 0.0000E + 00, and the function’s optimal value was less than 10–30, and the calculation time is 4.5s. The variance on the fixed dimensional function was 0.0000E + 00 and the optimal value of the function was 10 − 3.5,and the calculation time is 4.7s. The algorithmic curve all digging depth was maximum and convergence speed was fastest. The microgrid group system had the lowest economic cost of 4029.32 yuan in the grid-connected mode and 3343.39 yuan in the off-grid mode. The study proves that the energy coordination and economic management of this strategy are greatly optimized, which can effectively protect the energy storage equipment and guarantee the smooth power consumption of the system. This provides an innovative theoretical basis for optimization scheduling of microgrid group.

Problem

With the rapid development of social economy, the problem of line losses in distribution networks gradually becomes prominent, which directly affects the efficiency and economy of power systems.

Methodology

In order to reduce line losses, a loss optimization model for low and medium voltage distribution networks based on an improved Gray Wolf optimization support vector machine is proposed. The optimization model introduces a dimensional learning strategy based on the original model to enhance the adaptability and robustness of the model.

Results

The experimental results show that the Mean Absolute Percent Error (MAPE) of the proposed algorithm is 8.62%, the Mean Absolute Error (MAE) is 1.30% and the Root Mean Square Error (RMSE) is 2.26%. Compared with the traditional Gray Wolf Optimized Support Vector Machine, the errors of the improved model are reduced by 15.27%, 3.33% and 4.70%, respectively.

Contributions

Our study demonstrates that extracellular vesicles secreted by the gut microbiota can influence the nervous system via the microbial-gut-brain axis. Furthermore, we found that the extracellular vesicles secreted by the gut microbiota from the probiotic group exert a beneficial therapeutic effect on anxiety and hippocampal neuroinflammation.

The rapid growth of electric vehicles (EVs) demands a robust and efficient charging infrastructure. To address this, we propose a particle swarm optimization algorithm designed for optimal placement and sizing of EV charging stations. This study hypothesizes that comprehensive consideration of uncertainties in vehicle types, user behaviors, road dynamics, and environmental impacts will enhance infrastructure effectiveness. Our method integrates data from road networks, driver patterns, station owners, and EV manufacturers to meet diverse charging requirements. Results indicate that 14 fast charging stations are needed along the studied freeway, with a total installation cost of $289,820 and annual operational costs of $4,223,050, leading to annual CO₂ emissions of 1,843,572.57 kg. This strategic approach balances technical, environmental, and economic criteria, providing an essential tool for policymakers and urban planners in establishing sustainable EV charging networks.

Accurate crude oil price forecasting is crucial for various economic activities, including energy trading, risk management, and investment planning. Although deep learning models have emerged as powerful tools for crude oil price forecasting, achieving accurate forecasts remains challenging. Deep learning models’ performance is heavily influenced by hyperparameters tuning, and they are expected to perform differently under various circumstances. Furthermore, price volatility is also sensitive to external factors such as world events. To address these limitations, we propose a hybrid approach that integrates metaheuristic optimisation with an ensemble of five widely used neural network architectures for time series forecasting. Unlike existing methods that apply metaheuristics to optimise hyperparameters within the neural network architecture, we exploit the GWO metaheuristic optimiser at four levels: feature selection, data preparation, model training, and forecast blending. The proposed approach has been evaluated for forecasting three-ahead days using real-world Brent crude oil price data, and the obtained results demonstrate that the proposed approach improves the forecasting performance measured using various benchmarks, achieving 0.000127 of MSE.

The composite cylindrical wind turbine foundation is characterized by its large-diameter cylindrical base, which offers superior anti-overturning capability, and it is widely used in the soft soil seabed of Jiangsu, China. Due to its complex structural form, the local scour under combined waves and current significantly differs from that of monopile foundations. However, research on the scour characteristics specific to composite cylindrical wind turbine foundations remains scarce. A numerical model for local scour of wind turbine foundations was established in this study, which was verified with the field-measured scour data. A series of numerical simulations of local scour depths for composite cylindrical wind turbine foundations under various water depths and wave-current combinations were conducted. The simulation results indicate that the wake vortex shedding caused by the complex structural form leads to the local scour around the composite cylindrical wind turbine foundation; the normalized scour depth increases with the Keulegan-Carpenter number and the relative current strength; when the relative current strength is greater than 0.6, the influence of the Keulegan-Carpenter number on scour depth tends to be weakened; similarly, as the Keulegan-Carpenter number increases, the effect of the relative current strength on scour depth gradually diminishes. A scour equation of the composite cylindrical wind turbine foundation is suggested to predict the local scour in fine sand bed under waves and current.

With the increasing complexity of the distribution network, the proportion of abnormal data in the monitoring data of the distribution network and its daily work is extremely low. Traditional clustering analysis methods are difficult to effectively solve the imbalance problem. Therefore, this paper introduces the influence parameters that can adaptively adjust the cluster center of local samples in the boundary area, and improves the cluster center update formula, and proposes a method of distribution network operation state evaluation based on the local blur measurement of the boundary region. The research results found that the five evaluation indicators of the proposed algorithm were 112, 0, 2, 26, and 5, respectively, all of which were superior to the comparison algorithms. The research results showed that the cluster center update optimization method based on local fuzzy measure in boundary region could effectively reduce the negative impact of the edge region occupied by most clusters on its clustering effect, so that the cluster center was always in an ideal position. At the same time, the example results showed that the research method had a risk prediction of 0.91 for power outage networks, which was close to the real situation and had high accuracy. It can provide reference for the operation and maintenance work of power grid personnel, eliminate hidden dangers in advance, and ensure the safe operation of the power grid.

This study aims to advance the development of the active distribution network (ADN) by optimizing resource allocation across different stages to enhance overall system performance and economic benefits. First, an ADN optimization model is constructed based on a two-stage robust optimization approach. The first stage focuses on determining optimal decision variables within the uncertainty set, while the second stage adjusts control variables based on the initial stage decisions. This model effectively addresses source-load uncertainties while preserving the flexibility and adaptability of decision-making solutions. Additionally, this study explores uncertainty models that incorporate correlation factors. The IEEE33-node model is employed to validate the effectiveness and superiority of the proposed optimization strategy through numerical simulations. Simulation results demonstrate that Model 3 comprehensively accounts for photovoltaic and wind turbine generator planning by optimizing their capacity configurations, leading to a 23% increase in distributed generation (DG) penetration. During high-load periods (e.g., 13:00 and 16:00), DG output reaches 47% and 50% of the demand load, underscoring the critical role of DG in supporting the power grid during peak hours. Overall, the proposed two-stage optimization strategy considers source-load uncertainties, significantly reducing economic costs, enhancing DG output, and improving overall system performance. In scenarios with correlated uncertainties, the optimized results exhibit greater accuracy and reliability, providing robust support for the planning and operation of practical distribution networks.