Energy Informatics最新文献_第6页

Charging pile fault prediction method combining whale optimization algorithm and long short-term memory network 鲸鱼优化算法与长短期记忆网络相结合的充电桩故障预测方法

Energy Informatics Pub Date : 2025-05-20 DOI: 10.1186/s42162-025-00530-8

Yansheng Huang, Atthapol Ngaopitakkul, Suntiti Yoomak

{"title":"Charging pile fault prediction method combining whale optimization algorithm and long short-term memory network","authors":"Yansheng Huang, Atthapol Ngaopitakkul, Suntiti Yoomak","doi":"10.1186/s42162-025-00530-8","DOIUrl":"10.1186/s42162-025-00530-8","url":null,"abstract":"<div><p>As the world’s energy structure is gradually changing, the automotive industry is shifting its focus to new energy vehicles in an effort to improve the performance and service life of the charging pile. To solve the problem that traditional models tend to fall into locally optimal solutions (i.e., the model optimization process stays in the non-optimal regional minimum) in complex parameter space, the study innovatively proposes a hybrid prediction model that combines the whale optimization algorithm with the gated recurrent unit-long short-term memory neural network. By introducing the whale optimization mechanism to globally optimize the key parameters of the neural network, the method improved the model’s ability to model complex time series data. Moreover, the method also effectively avoided the problem of traditional methods falling into local optimal solutions, thus improving the training efficiency and generalization ability while maintaining the model accuracy. It took only 21 s to complete the training of 600 samples, and the prediction accuracy was as high as 91%. In the four classes of fault classification experiments, the proposed model performs well in classification accuracy in all classes, showing strong multi-class fault recognition capability. Therefore, the fault prediction model developed in this study can accurately and effectively identify and predict charging pile faults, and shows high performance. This not only provides a strong theoretical foundation for the application of deep learning in charging pile fault prediction, but is also of great significance in terms of reducing operation and maintenance costs, supporting energy structure transformation, and promoting green development.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00530-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100189","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}

引用次数: 0

Measurement error evaluation method for voltage transformers in distribution networks based on self-attention and graph convolutional networks 基于自关注和图卷积网络的配电网电压互感器测量误差评估方法

Energy Informatics Pub Date : 2025-05-20 DOI: 10.1186/s42162-025-00525-5

Xiujuan Zeng, Tong Liu, Huiqin Xie, Dajiang Wang, Jihong Xiao

{"title":"Measurement error evaluation method for voltage transformers in distribution networks based on self-attention and graph convolutional networks","authors":"Xiujuan Zeng, Tong Liu, Huiqin Xie, Dajiang Wang, Jihong Xiao","doi":"10.1186/s42162-025-00525-5","DOIUrl":"10.1186/s42162-025-00525-5","url":null,"abstract":"<div><p>Accurately evaluating the error of voltage transformers in distribution networks is crucial for the safe operation of power systems and the fairness of electricity trade. This paper uses the connection relationship between distribution transformers and voltage transformers to predict the secondary voltage of voltage transformers through the secondary voltage of transformers, constructing a voltage transfer characteristic model between the two to achieve accurate evaluation of voltage transformer errors. To address the challenge of extracting complex nonlinear features from multivariate electrical data, a combined model of a self-attention mechanism and a graph convolutional network (GCN) is proposed. The self-attention mechanism captures global dependencies among power parameters, while the GCN effectively constructs the multivariate data structures in distribution networks. By integrating both approaches, the model can fully extract the intrinsic features of the data as well as the hidden dependency information between data points. Additionally, to prevent gradient vanishing as the combined model’s structure deepens, a multi-head residual structure is introduced to enhance the self-attention mechanism. Experimental results show that compared to a single model, the proposed combined model reduces the mean squared error by 82.35% and increases the coefficient of determination R<sup>2</sup> by 9.07%, demonstrating significant accuracy advantages in voltage transformer error evaluation.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00525-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100145","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}

引用次数: 0

Day-ahead photovoltaic power forecasting with multi-source temporal-feature convolutional networks 基于多源时间特征卷积网络的日前光伏发电预测

Energy Informatics Pub Date : 2025-05-16 DOI: 10.1186/s42162-025-00531-7

Ziming Ouyang, Zhaohui Li, Xiangdong Chen

{"title":"Day-ahead photovoltaic power forecasting with multi-source temporal-feature convolutional networks","authors":"Ziming Ouyang, Zhaohui Li, Xiangdong Chen","doi":"10.1186/s42162-025-00531-7","DOIUrl":"10.1186/s42162-025-00531-7","url":null,"abstract":"<div><p>Photovoltaic (PV) power forecasting technology enhances the absorption capacity of renewable energy. However, the PV power generation process is highly sensitive to fluctuations in weather conditions, making accurate forecasting challenging. In this paper, we propose a composite data augmentation method and a model that can effectively utilize the augmented data. The PV power generation process has a fluctuating nature over time, so an augmented sample set with temporal correlation was created. This was achieved by reconstructing meteorological features and screening measurements similar to historical meteorological conditions. To improve the feature extraction capability for multi-source heterogeneous data and the temporal modeling capability for fine-grained periods, a multi-source temporal-feature convolutional networks (MSTFCN) model is proposed. MSTFCN employs parallel convolution to capture local temporal patterns and improves global feature representation via a channel attention mechanism. Based on this, redundant information is suppressed by a cascading channel compression approach, and a temporal segmentation strategy is applied to model fine-grained temporal features. We conducted experiments on two publicly available datasets, and the results demonstrate that the proposed data augmentation method effectively improves the forecasting performance of the deep learning model. Moreover, MSTFCN achieves higher forecasting accuracy and exhibits stronger environmental adaptability than the compared models.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00531-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073727","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}

引用次数: 0

Power grid energy storage system planning method based on optimized butterfly algorithm

Energy Informatics Pub Date : 2025-05-13 DOI: 10.1186/s42162-025-00528-2

Xiang Yin, Xiaojun Zhang, Fuhai Cui

{"title":"Power grid energy storage system planning method based on optimized butterfly algorithm","authors":"Xiang Yin, Xiaojun Zhang, Fuhai Cui","doi":"10.1186/s42162-025-00528-2","DOIUrl":"10.1186/s42162-025-00528-2","url":null,"abstract":"<div><p>In response to the power supply security of power grid system caused by a large number of clean energy connected to the distribution network, based on the grid side energy storage investors, the butterfly optimization algorithm is improved by combining the dynamic switching probability coordination algorithm and the dynamic Gaussian mutation strategy. A Distributed Energy Storage System (DESS) planning for power grid is constructed. The results showed that the research model had high stability and convergence accuracy, which was superior to comparison algorithms. When two DESS power stations were connected to nodes 4 and 32, with rated powers of 1.63 MW and 1.78 MW, and rated capacities of 5.71 MWh and 7.33 MWh, the annual benefits of capacity decision, location decision, and system were 783,000 RMB, 394,400 RMB, and 388,600 RMB, respectively. This showed that the research method could help operators obtain the maximum equal life return and meet their investment expectations. Before connecting to DESS, the overall voltage deviation of each typical state decreased by 5.28 p.u., 5.79 p.u., 2.84 p.u., and 2.37 p.u., and the overall active power loss of the daily power grid decreased by 1.41 MW, 1.83 MW, 1.79 MW, and 1.68 MW, respectively, indicating significant optimization effects. The research results indicate that the proposed solution can improve the overall stability and economy of the power grid, with strong applicability. This is of great significance for leveraging the supportive role of energy storage in safe operation and promoting the large-scale application of energy storage systems.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00528-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944356","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}

引用次数: 0

A case study on the calculation of carbon emissions over the entire life cycle of commercial office buildings based on measured data

Energy Informatics Pub Date : 2025-05-13 DOI: 10.1186/s42162-025-00527-3

Meijiu Zhao, Xiaochun Zhao

{"title":"A case study on the calculation of carbon emissions over the entire life cycle of commercial office buildings based on measured data","authors":"Meijiu Zhao, Xiaochun Zhao","doi":"10.1186/s42162-025-00527-3","DOIUrl":"10.1186/s42162-025-00527-3","url":null,"abstract":"<div><p>Against the backdrop of the “dual carbon” strategy, building carbon emissions have been incorporated into the construction review process. Currently, the calculation and accounting of carbon emissions throughout the entire building lifecycle have become a hot and challenging issue in the field of building carbon emissions. Commercial office buildings, due to their large scale, high energy consumption, and significant carbon emission base, have become a key area for energy conservation and carbon reduction in public buildings. According to the building lifecycle carbon emission assessment system, the lifecycle of commercial office buildings can be divided into four stages: production of building materials, construction, operation and maintenance, and dismantling and recycling. This study takes an existing commercial office building in Beijing, China, as a case study, and based on data from energy audit reports, calculates the carbon emissions of each lifecycle stage using national standards and relevant software, and discusses the factors affecting building carbon emissions. At the same time, a comparative analysis of the differences between Chinese and Western national standards is conducted. Ultimately, strategies to reduce building carbon emissions are proposed. This study is of reference value for the precise calculation of carbon emissions throughout the lifecycle of commercial office buildings.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00527-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944357","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}

引用次数: 0

Optimizing energy flow in advanced microgrids: a prediction-independent two-stage hybrid system approach 优化先进微电网的能量流：一种独立于预测的两阶段混合系统方法

Energy Informatics Pub Date : 2025-05-12 DOI: 10.1186/s42162-025-00523-7

Mohamad Javad Mohamadi, Mohammad Tolou Askari, Mahmoud Samiei Moghaddam, Vahid Ghods

{"title":"Optimizing energy flow in advanced microgrids: a prediction-independent two-stage hybrid system approach","authors":"Mohamad Javad Mohamadi, Mohammad Tolou Askari, Mahmoud Samiei Moghaddam, Vahid Ghods","doi":"10.1186/s42162-025-00523-7","DOIUrl":"10.1186/s42162-025-00523-7","url":null,"abstract":"<div><p>This paper presents a two-stage optimization framework for long-term energy management in microgrids, aiming to efficiently integrate various energy sources, storage systems, and consumption elements while addressing uncertainties in load demand and renewable generation. The framework consists of an offline optimization stage and an online optimization stage, each with distinct roles to balance long-term planning and real-time adaptability. In the offline stage, a robust two-stage mixed-integer linear programming (MILP) model is used to set annual targets for the state of charge (SoC) of energy storage systems. This stage applies a min-max-min approach to optimize for worst-case scenarios, establishing a cost-effective and reliable baseline plan that reduces dependency on conventional power sources and minimizes load deficits. The online stage, on the other hand, employs a new online convex optimization model that dynamically adjusts energy storage and dispatch decisions based on real-time data, allowing the microgrid to respond flexibly to fluctuations in demand and renewable generation. Simulation results using the Elia and North China datasets demonstrate the effectiveness of this two-stage approach. Offline optimization achieved up to 25% cost savings and reduced unmet demand by up to 99%, providing a stable foundation for efficient energy management. The online optimization stage further improved system responsiveness, minimizing reliance on backup generators and enhancing load reliability. This combined framework offers a comprehensive solution for optimizing microgrid performance, balancing predictive planning with real-time adaptability in complex, variable energy environments.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00523-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143938169","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}

引用次数: 0

Hybrid energy storage device based on multi-port transformer and direct current bus connection 基于多端口变压器和直流母线连接的混合储能装置

Energy Informatics Pub Date : 2025-05-08 DOI: 10.1186/s42162-025-00520-w

Xiu Zheng, Haixu Chen, Jiyang Zhang, Xiaohe Zhao, Dantong Wang

{"title":"Hybrid energy storage device based on multi-port transformer and direct current bus connection","authors":"Xiu Zheng, Haixu Chen, Jiyang Zhang, Xiaohe Zhao, Dantong Wang","doi":"10.1186/s42162-025-00520-w","DOIUrl":"10.1186/s42162-025-00520-w","url":null,"abstract":"<div><p>In the context of energy management during digital transformation, traditional energy storage devices face challenges in multi-source coordination and efficient management. The key issue for system optimization is how to stabilize the management of multiple energy storage units. To address this, the study innovatively proposes a Hybrid Energy Storage System integrating a Multi-Port Transformer and Direct Current Bus. By constructing multi-port control factors, the system achieves coordinated optimization of the energy storage units, through dynamic adjustment of multi-port control factors and energy conversion matrices, the system can flexibly allocate power output from various energy storage units according to load demands, ensuring stable system operation. Experimental results in a microgrid system show that the integrated control system has a response time of 2.3 ms under 80% load, significantly outperforming the Proportional Integral Control (8.7 ms) and during the energy storage unit switching process, the voltage fluctuation rate is only 0.8% with a switching time of just 1.8 ms, and system stability reaching 98.5%. Under high-load conditions, the energy conversion efficiency is 96.8%, and the power distribution error is only 1.2%. Compared to traditional energy storage devices, the initial investment cost of this device is reduced by 7.4%, and the annual maintenance cost is reduced by 21.7%. These results indicate that the improved hybrid energy storage device not only possesses excellent energy management capabilities but also significantly reduces operational costs and environmental impact. The study provides an efficient technical solution for managing complex energy systems, which is of great significance for promoting smart grid construction and achieving green, low-carbon goals.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00520-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925705","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}

引用次数: 0

The relationship between economic growth and carbon emissions based on the combination of graph neural network and wavelet transform 基于图神经网络与小波变换相结合的经济增长与碳排放关系研究

Energy Informatics Pub Date : 2025-05-08 DOI: 10.1186/s42162-025-00521-9

Sen Wang

{"title":"The relationship between economic growth and carbon emissions based on the combination of graph neural network and wavelet transform","authors":"Sen Wang","doi":"10.1186/s42162-025-00521-9","DOIUrl":"10.1186/s42162-025-00521-9","url":null,"abstract":"<div><p>The purpose of this study is to explore the impact of dynamic adaptation of corporate innovation culture and market demand on corporate sustainable development and the differences in corporate types and regions. The research sample covers 150 listed companies and 100 non-listed companies in eight industries and three economic regions: the eastern coastal area, the rise of central China, and the development of the western region from 2015 to 2020. The theoretical framework is constructed using the system dynamics model, and the empirical methods of multivariate regression analysis such as ordinary least squares, fixed effect model, and instrumental variable method are used for research. The main findings include that there is a significant positive synergistic relationship between corporate innovation culture and market demand, and there are differences in development models among enterprises of different types and regions. These results have important policy implications and can provide reference for the National Development and Reform Commission, the Ministry of Industry and Information Technology and other relevant departments to formulate policies such as industrial guidance and innovation incentives, help enterprises achieve sustainable development, and enhance the competitiveness of the national industry.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00521-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925706","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}

引用次数: 0

Machine learning applications in energy systems: current trends, challenges, and research directions 机器学习在能源系统中的应用：当前趋势、挑战和研究方向

Energy Informatics Pub Date : 2025-05-07 DOI: 10.1186/s42162-025-00524-6

Saad Aslam, Pyi Phyo Aung, Ahmad Sahban Rafsanjani, Anwar P. P. Abdul Majeed

{"title":"Machine learning applications in energy systems: current trends, challenges, and research directions","authors":"Saad Aslam, Pyi Phyo Aung, Ahmad Sahban Rafsanjani, Anwar P. P. Abdul Majeed","doi":"10.1186/s42162-025-00524-6","DOIUrl":"10.1186/s42162-025-00524-6","url":null,"abstract":"<div><p>The paradigm shift towards Smart Grids, Smart Buildings, Smart Monitoring, and Operation has driven researchers to propose innovative solutions for designing and maintaining energy systems. Although the integration of Renewable Energy Sources (RES) supports sustainability goals, it also introduces vulnerabilities to unpredictable challenges such as grid stability, energy storage requirements, and infrastructure modernization. Machine Learning (ML) has emerged as a transformative tool to address these challenges, offering opportunities to enhance energy efficiency, and system design in alignment with Sustainable Development Goals (SDGs). The emphasis on these goals necessitates the study of new system designs that prioritize energy efficiency. Building on its proven success, researchers are increasingly adopting ML-driven approaches to accelerate advances in energy systems. This work presents a detailed review of current ML-driven research trends in energy systems, outlines the associated challenges, and provides potential research directions and recommendations. Unlike the existing literature, which focuses primarily on ML applications in the RES domain, this study offers a holistic perspective on ML-driven approaches across various aspects of energy systems, including energy policy and sustainability. It aims to serve as a comprehensive resource, bridging the gap between research advancements and practical implementations in energy systems through ML-driven innovation.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00524-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913899","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}

引用次数: 0

Correction: Research on building energy consumption prediction algorithm based on customized deep learning model 更正：基于定制深度学习模型的建筑能耗预测算法研究

Energy Informatics Pub Date : 2025-05-01 DOI: 10.1186/s42162-025-00509-5

Zheng Liang, Junjie Chen

引用次数: 0