Energy Informatics最新文献

{"title":"Wind power generation prediction using LSTM model optimized by sparrow search algorithm and firefly algorithm","authors":"Wenjing Zhang,&nbsp;Hongjing Yan,&nbsp;Lili Xiang,&nbsp;Linling Shao","doi":"10.1186/s42162-025-00492-x","DOIUrl":"10.1186/s42162-025-00492-x","url":null,"abstract":"<div><p>As an important renewable energy source, wind power generation is highly stochastic and uncertain due to various environmental factors affecting its output. To raise the accuracy of wind power generation prediction, a bidirectional long short-term memory network combination model based on sparrow search algorithm and firefly algorithm optimization is designed. The model first employs a bidirectional long short-term memory network to capture the long-term dependency features of time series, and uses random forests for nonlinear modeling and feature selection. Then, the sparrow search algorithm and firefly algorithm are combined to optimize the hyperparameter configuration, improving the predictive performance and global search ability of the model. The findings denote that the accuracy of the designed model reaches 98.5%, with a mean square error as low as 0.005 and a prediction time as short as 0.18 s. The simulation analysis results show that the predicted values of the developed model almost coincide with the actual values, with small errors. The research outcomes denote that the optimized model greatly raises the accuracy and efficiency of wind power generation prediction, and has good application prospects.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00492-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time monitoring and energy consumption management strategy of cold chain logistics based on the internet of things","authors":"Kang Wang,&nbsp;Ning Du","doi":"10.1186/s42162-025-00493-w","DOIUrl":"10.1186/s42162-025-00493-w","url":null,"abstract":"<div><p>With the rapid development of the cold chain logistics industry, its high energy consumption and low operational efficiency have become increasingly prominent, seriously restricting the sustainable development of the industry. This study focuses on this and proposes a real-time monitoring system for cold chain logistics based on the Internet of Things. It combines the long short-term memory network (LSTM) and the particle swarm optimization (PSO) algorithm to build an energy consumption management strategy. Through the distributed system architecture design, a variety of data transmission protocols are used to ensure real-time and stable data collection and transmission, and to achieve accurate monitoring of key environmental factors in the transportation and storage of cold chain logistics. The experiment was carried out in a simulated cold chain logistics scenario. The data set covers multiple types of sensor data and is compared with multiple baseline models. The results show that compared with the traditional cold chain logistics system, this system significantly improves energy efficiency, reduces energy consumption by about 20%, increases temperature and humidity control accuracy to 94% respectively, improves transportation efficiency, and shortens transportation time by 8.33%. At the same time, the combination of LSTM and PSO algorithms optimizes energy consumption prediction and equipment scheduling, and the equipment group collaborative optimization strategy enhances system stability. This study confirms that the real-time monitoring and energy consumption management strategy based on the Internet of Things can effectively improve the economic and environmental benefits of the cold chain logistics system.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00493-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal scheduling of clean energy storage and charging integrated system by fusing DE algorithm and kernel search algorithm","authors":"Xinhua Wang,&nbsp;Yujie Jia,&nbsp;Hao Su,&nbsp;Hua Dang,&nbsp;Songfu Lu","doi":"10.1186/s42162-025-00494-9","DOIUrl":"10.1186/s42162-025-00494-9","url":null,"abstract":"<div><p>In the context of rapid developments in artificial intelligence and the clean energy industry, the optimal scheduling of clean energy storage and charging systems has become increasingly prominent. This study proposes an optimal scheduling method that integrates Differential Evolution (DE) and Kernel Search Optimization (KSO) algorithms. By incorporating DE’s mutation, crossover, and selection operations into the KSO framework, the method effectively avoids local optima while retaining KSO’s advantages in handling complex structures and large-scale data. Experimental results demonstrate that the convergence speed of the fusion algorithm is improved by 34.2%, 30.8%, 28.6%, and 23.4% over four other algorithms for hybrid functions, and by 56.7%, 52.9%, 25.3%, and 21.4% for combined functions. Additionally, the utilization of renewable energy increased from 40% to nearly 70% within 24 h. It can be seen that the convergence speed and renewable energy utilization of the fusion algorithm are significantly improved compared with the four baseline methods, highlighting its effectiveness in large-scale clean energy systems. This research provides an effective scheduling strategy for optimizing clean energy storage and charging systems. This study provides an effective scheduling strategy for optimizing clean energy storage and charging systems, and supports scalable and efficient energy management of urban and rural energy grids. The results show that the optimization of the integrated charging system can not only achieve optimal scheduling in a shorter time, but also reduce operating costs and resource waste, and effectively improve the overall operating efficiency of the energy system. Research to promote the efficient use of renewable energy will help reduce dependence on fossil fuels, thereby reducing greenhouse gas emissions and environmental pollution, which will have a positive impact on achieving the Sustainable Development goals and addressing climate change, and promote a win-win situation for the economy and the environment.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00494-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PIDE: Photovoltaic integration dynamics and efficiency for autonomous control on power distribution grids","authors":"Gökhan Demirel,&nbsp;Natascha Fernengel,&nbsp;Simon Grafenhorst,&nbsp;Kevin Förderer,&nbsp;Veit Hagenmeyer","doi":"10.1186/s42162-025-00489-6","DOIUrl":"10.1186/s42162-025-00489-6","url":null,"abstract":"<div><p>With a focus on larger rooftop or utility-scale solar systems, there is a lack of research on the potential impact of mini photovoltaic (MPV) systems, often referred to as balcony power plants. This work analyzes the impact of varying concentrations of MPV systems, on the stability and control of low-voltage (LV) grids. We offer a comprehensive technical assessment of MPV within a distribution grid and quantify their effects on power quality, losses, transformer loading, and the performance of other inverter-based voltage-regulation devices. For this purpose, this paper introduces the open-source Python-based framework PIDE (Photovoltaic Integration Dynamics and Efficiency), a tool for simulating the integration of distributed energy resources (DER)s and evaluating their impact on autonomous reactive power control in the distribution grid. Our case studies include a one-year sensitivity analysis based on Monte Carlo simulations, compare distributed and decentralized DER control strategies, and demonstrate the role of autonomous inverters in providing ancillary services. With the growing use of battery energy storage (BES) systems in LV grids for these services, the need for adaptable DER control strategies becomes increasingly evident. Our results show that high MPV penetration increases mean transformer load by up to 3%, line load by 2.5% and total power losses by around 17%.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00489-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Demand response and energy dispatch system for intelligent buildings based on improved MOALO algorithm","authors":"Weiwei Han","doi":"10.1186/s42162-025-00490-z","DOIUrl":"10.1186/s42162-025-00490-z","url":null,"abstract":"<div><p>As the rate of energy consumption in intelligent buildings increases, the uneven distribution of energy among different devices in intelligent buildings leads to further acceleration of energy consumption. The study suggested designing an energy dispatch system for intelligent buildings based on the enhanced multi-objective ant-lion optimizer algorithm in an attempt to address the issue that the conventional energy dispatch system for intelligent buildings is unable to carry out energy dispatch in accordance with the electricity price and incentives. The initialization of different energy data parameters was carried out by the multi-objective ant-lion optimizer algorithm, and the variance crossover operation of the data parameters was carried out by the differential evolution algorithm. Based on the improved multi-objective ant-lion optimizer algorithm, a demand response model was constructed, and the energy dispatch system of intelligent buildings was constructed accordingly. The results revealed that the area under the PR curve of the improved multi-objective ant-lion optimizer algorithm was 0.9653, which was significantly higher than the other three algorithms. The root mean square error and the mean absolute error of the algorithm were 0.839 and 0.648, respectively. In the experiments on the practical application of the dispatch system, it was found that the average power of the dispatched energy sources was significantly lower than that of the non-dispatched energy power distribution. The aforementioned findings indicate the suggested approach can more effectively schedule various energy sources in intelligent buildings, offering technical assistance in the area of energy dispatch.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00490-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid energy storage system for intelligent electric vehicles incorporating improved PSO algorithm","authors":"Hui Shu","doi":"10.1186/s42162-025-00488-7","DOIUrl":"10.1186/s42162-025-00488-7","url":null,"abstract":"<div><p>Existing energy storage system is difficult to balance the energy distribution and dynamic response efficiency issues of lithium-ion batteries and supercapacitor, resulting in low energy utilization. Therefore, the study proposes a hybrid energy storage system for intelligent electric vehicles incorporating improved particle swarm optimization. The study analyzes the relationship between vehicle driving speed and power demand through equivalent model, constructs an objective function containing power demand and state of charge, and uses an improved algorithm for optimization and solution. The performance test results indicated that the proposed improved algorithm exhibited the fastest convergence speed by rapidly decreasing the objective function value and approximating the optimal solution within the first 20 iterations in both single-peak and multi-peak functions. The simulation experiments were validated under urban working conditions and highway working conditions, respectively. The results indicated that the energy efficiency in both working conditions was improved to 92.5% and 94.9%, respectively. In addition, good results were achieved in the contribution of supercapacitor, which were 27.2% and 29.6%, respectively. In the test results based on HIL environment, the system proposed by the research institute can also maintain energy efficiency of over 80% under extreme conditions. The findings support the optimal design of intelligent electric vehicle energy storage systems both theoretically and practically, showing that the study’s revised algorithm performs well in both energy allocation efficiency and dynamic response performance.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00488-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The design of a real-time monitoring and intelligent optimization data analysis framework for power plant production systems by 5G networks","authors":"Xihong Chuang,&nbsp;Le Li,&nbsp;Lei Zhu,&nbsp;Mingyi Wei,&nbsp;Yongsheng Qiu,&nbsp;Yanqing Xin","doi":"10.1186/s42162-025-00487-8","DOIUrl":"10.1186/s42162-025-00487-8","url":null,"abstract":"<div><p>The current power plant production systems face issues such as insufficient monitoring accuracy, data transmission delays, and low energy utilization efficiency. In response, this study proposes a real-time monitoring and intelligent data analysis system based on Fifth-Generation Mobile Communication Network (5G) technology. Building upon an analysis of the limitations inherent in traditional systems, the experiment capitalizes on the extensive connectivity capabilities of 5G to design an intelligent monitoring architecture tailored for power plant production environments. To enhance system performance, the study introduces an innovative resource scheduling and data analysis model that combines an improved Hybrid Advantage Actor-Critic (A3C) algorithm with a Dueling Deep Q-Network (DQN) algorithm. This model integrates the global optimization capabilities of the A3C algorithm with the efficient learning mechanism of the Dueling DQN algorithm to optimize communication resource scheduling and energy storage management within a 5G Cloud Radio Access Network (C-RAN) environment. Simulation experiments demonstrate that this approach significantly improves system energy efficiency, optimizes resource utilization, and reduces energy waste. The results show that data transmission delays decreased by 25%, energy utilization increased by 18.25%, and renewable energy consumption rose by 12.55%. This study offers a new technical approach for the intelligent upgrade and green, efficient operation of power plant production systems, providing both theoretical and practical support for the optimization of power systems in the 5G era.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00487-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of distribution network reliability based on distribution automation technology","authors":"Liao Qinglong,&nbsp;Wu Xiaodong,&nbsp;Xie Song,&nbsp;Xaio Xiang,&nbsp;Peng Bo","doi":"10.1186/s42162-025-00478-9","DOIUrl":"10.1186/s42162-025-00478-9","url":null,"abstract":"<div><p>The growing complexity and need for electricity in contemporary grids have resulted in an increased dependence on Distribution Automation Technology (DAT) to improve the effectiveness and reliability of distribution networks. Automation technologies, like smart sensors and fault detection systems, are critical for enhancing operational efficiency and lowering power outages in distribution networks. This study investigates the influence of distribution automation on the dependability of electricity networks, concentrating on important functional metrics and their relationship with network efficiency. Objectives: The main objective of this research is to examine the factors that influence the reliability of distribution networks, with a focus on distribution automation technology. This study uses a variety of efficiency indicators, like automation coverage, fault detection time, and consumer complaints, to discover the primary factors of network reliability. This paper introduced the Reliability-Optimized Meta-Learning Ensemble (ROME) algorithm, which seeks to predict the reliability category of various areas using these indicators. Methodology: This study utilizes the Distribution Network Reliability Dataset, which includes several areas with a variety of characteristics such as network age, automation coverage, smart sensor installation, power outages, fault detection time, and other operational metrics. The ROME algorithm is used, which integrates numerous base models (SVM, Random Forest, MLP) and a meta-learner (Gradient Boosting) to predict each region’s Reliability Category (High, Medium, Low). The dataset is thoroughly preprocessed, which includes mean and mode imputation, label encoding, standardization, and SMOTE balancing. Recursive Feature Elimination (RFE) is used for feature selection. Results: The findings show a strong correlation between automation coverage, fault detection time, and reliability category. When compared to traditional classification techniques, the ROME algorithm surpassed SVM, RF, MLP, and GB models with 94.7% accuracy, 0.18 Log-Loss, 91.2% Jaccard Index, 0.08% fall-out, and 95.3% specificity. Conclusion: This research emphasizes the value of distribution automation in improving network reliability. Utilities and grid operators can use the ROME algorithm to better predict and enhance network reliability. The results highlight the requirement for targeted investments in automation technologies, particularly in regions with lower reliability scores, to guarantee sustainable and effective electricity distribution.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00478-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving power distribution networks with dwarf mongoose optimization for improved photovoltaic incorporation in rural-urban settings","authors":"Guo Chen,&nbsp;JIA Honggang,&nbsp;Zeng Jian,&nbsp;Zhang Zhiqi,&nbsp;Zhou Xingxing","doi":"10.1186/s42162-025-00484-x","DOIUrl":"10.1186/s42162-025-00484-x","url":null,"abstract":"<div><p>This paper aimed to assess new connotations and characteristics of power distribution networks in new situations like integrating photovoltaic (PV) systems. Power system emission reduction is an ongoing subject of discourse, and solar energy production using PV is gaining popularity. Centralized and unidirectional systems, nevertheless, provide difficulties. An investigation is expected to comprehend the network’s design and PV integration capacity’s (PV-IC’s) responsiveness to subsequent generations.With an emphasis on low and medium-voltage networks, the paper presents a unique dwarf mongoose optimization (DMO)approachfor developing efficient network configurations. It analyzes the effect of network configuration on PV-IC.This study is experimented with MATLAB/Simulink platform based on the DMO technique. Different PV system numbers and peak powers, together with alternate providing substations, have been modeled for a certain set of load locations. The load time series computed shows rural-urban zones, and the proposed DMO is implemented on several topological generations. The computed results indicate that network topologies must be changed to accommodate raised solar energy production and PV-IC, with rural regions attaining up to 8.2 kW using TF (+). Our proposed DMO approach surpassed alternatives, with rural regions having a higher PV-based load of 190 kW compared to 120 kW in urban areas. Voltage control tactics, like RPC and Curt, benefit up to 55% of rural customers versus 15% in urban areas. Policy changes for household PV incorporation may be needed to maximize solar energy use.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00484-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy management in a microgrid equipped with an electric vehicle based on the internet of things considering responsive load","authors":"Mohamad Mahdi Erfani Majd,&nbsp;Reza Davarzani,&nbsp;Mahmoud Samiei Moghaddam,&nbsp;Ali Asghar Shojaei,&nbsp;Mojtaba Vahedi","doi":"10.1186/s42162-025-00475-y","DOIUrl":"10.1186/s42162-025-00475-y","url":null,"abstract":"<div><p>Advancements in renewable energy technologies have positioned microgrids as essential applications of the Internet of Things (IoT), necessitating innovative energy management systems. This study introduces a dual-layer energy trading framework designed to optimize interactions among interconnected microgrids and users. The upper layer focuses on energy exchanges between microgrids, while the lower layer manages transactions among local users. A novel Energy-Trading Management Algorithm (ETMA), based on the Meerkat Optimization Algorithm (MOA), is proposed to tackle the complexities of this system. By integrating a multiblockchain structure with a Delegated Proof of Reputation (DPoR) consensus protocol, the framework ensures secure and private transactions while incentivizing compliance among participants. Experimental validation with real-world data from Guizhou demonstrates significant improvements in efficiency and utility for both users and microgrid operators (MGOs) compared to traditional methods. This approach sets a new benchmark for scalable, secure, and efficient energy management in microgrid environments. </p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00475-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}