Energy Informatics最新文献

In Industry 4.0, integrating Cyber-Physical Systems (CPS) within wind energy infrastructures introduces significant cyber-attack vulnerabilities. This paper presents the Hybrid Adaptive Threat Detection and Response System (HATDRS), a novel security architecture designed to enhance the resilience of wind energy systems against evolving cyber threats. The HATDRS model integrates a hybrid machine learning approach, combining supervised logistic regression with adaptive learning mechanisms, providing real-time threat detection and mitigation. This approach was chosen for its ability to integrate labelled data with real-time unsupervised feedback, providing dynamic and accurate threat detection in wind energy systems. The model was evaluated against traditional Intrusion Detection Systems (IDS) and Machine Learning-based Anomaly Detection Systems (ML-ADS) across key metrics, including accuracy, detection rate, false positive rate, response time, System Security Index (SSI), energy loss, and cost-efficiency. The results demonstrate that the HATDRS model outperforms its counterparts, achieving an accuracy of 95.4% and a detection rate of 97.2% while maintaining the lowest false positive rate (3.1%) and response time (500 ms). Additionally, the model achieved the highest SSI value of 88.7, significantly reducing energy loss to 1.5% and improving cost-efficiency to 0.528. These findings underscore the robustness and efficiency of the HATDRS model in mitigating cyber-physical threats in wind energy systems, offering a scalable and effective solution for securing renewable energy infrastructures. Future work will explore further optimization and real-world testing to validate the system’s scalability across diverse energy environments.

Studying the impact of the electric vehicle supply chain on the environment is crucial for determining the future development direction of the industry. We have developed a method for evaluating the impact of supply chains on the environment based on a lifecycle framework. This method innovatively seeks the connection between the lifecycle process of physical products and the supply chain, and organizes the environmental impact assessment factors of the electric vehicle supply chain from three aspects: physical resources, power energy, and waste emissions, in order to construct an LCA fuzzy comprehensive evaluation model for the electric vehicle supply chain. For the first time, the research method of transforming qualitative analysis into quantitative data was introduced into the life cycle environmental impact assessment, and empirical research was conducted using the supply chain of sightseeing electric vehicles as an example. The results indicate that the scrapping stage of electric vehicles has the most severe impact on the environment. Strengthening research on strategies or technologies for handling waste batteries and automobiles is key to improving the environmental performance of the supply chain. This method breaks through the requirements and limitations of traditional life cycle assessment methods on data sources and parameters, avoids large-scale calculations that cannot be separated from subjective factors in traditional methods, simplifies the process of supply chain environmental impact assessment, shortens the evaluation time, and improves the efficiency of environmental impact assessment. It is more practical and has good application prospects.

In recent years, the new energy vehicle industry has developed rapidly. A fast diagnostic method based on Boosting and big data is proposed to address the low accuracy and efficiency of fault diagnosis in new energy vehicle power batteries. Boosting is a machine learning technique that combines multiple weak learners into a strong learner. Big data refers to large-scale, complex datasets that exceed traditional data processing capabilities. Firstly, analyze and preprocess the big data uploaded by the battery. Subsequently, the importance of indicators in the data was analyzed using the Random Forest algorithm (RF). Finally, three improved Boosting algorithms were proposed, namely Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting Tree (XGBoost), and Gradient Boosting Decision Tree (CatBoost). The experimental results indicate that the LightGBM model effectively detects anomalies in battery big data. The accuracy values of XGBoost, CatBoost, and LightGBM are 97.84%, 98.57%, and 99.16%, respectively. The recall rates of XGBoost, CatBoost, and LightGBM models are all 1. The F1 values of GBoost, CatBoost, and LightGBM are 0.873, 0.983, and 0.985, respectively. The power battery is the core component of new energy vehicles, and its safety performance directly affects the operational safety of the vehicle. Timely identification and diagnosis of battery faults can effectively reduce potential accidents such as battery overheating and short circuits. Research can achieve real-time monitoring and timely reminders of potential faults. By early detection of issues such as battery overheating and voltage imbalance, this method can effectively reduce the risk of serious safety accidents and improve the overall operational reliability of new energy vehicles during driving.

As environmental issues become more prominent, enterprises increasingly focus on reducing low-carbon emissions through green investment. Simultaneously, governments have implemented various low-carbon emission reduction strategies. This study assesses how varying low-carbon emission reduction strategies influence green investment efficiency in enterprises. The study employed the widely used a slack-based model (SBM) in efficiency estimation to analyze the variations in green investment efficiency under command-based, incentive-based, and public-based strategies. The findings revealed that the coefficient for the command-based strategy was − 0.456, the coefficient for the incentive-based strategy was 0.555, and the coefficient for the public-based strategy was 0.133. All coefficients were statistically significant at the 1% level. The regression analysis results aligned with hypotheses H1-H3, indicating that the command-based strategy hampered green investment efficiency while the incentive-based and public-based strategies enhanced it. These results demonstrate that diverse low-carbon emission reduction strategies yield varying impacts on enterprises’ green investment efficiency. The research results can provide a basis for policy-making in the actual government environmental protection departments.

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.