Energy Informatics最新文献_第8页

Physics-informed voting ensemble for solar power generation forecasting: integrating domain knowledge with machine learning 基于物理的太阳能发电预测投票集合：将领域知识与机器学习相结合

Energy Informatics Pub Date : 2026-01-02 DOI: 10.1186/s42162-025-00604-7

Manimaran Naghapushanam, Baskaran Jeevarathinam, C. Sankari

{"title":"Physics-informed voting ensemble for solar power generation forecasting: integrating domain knowledge with machine learning","authors":"Manimaran Naghapushanam, Baskaran Jeevarathinam, C. Sankari","doi":"10.1186/s42162-025-00604-7","DOIUrl":"10.1186/s42162-025-00604-7","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate solar power generation forecasting is essential for grid stability and renewable energy integration. This paper presents an enhanced solar power forecasting system achieving 94.95% accuracy (<span>(hbox {R}^{2})</span>) using a voting ensemble approach combined with physics-informed feature engineering. The methodology transforms 21 meteorological variables from the Kaggle Solar Energy Power Generation Dataset into 41 engineered features incorporating solar geometry, atmospheric physics, and temporal dynamics. The proposed voting ensemble combines Gradient Boosting Regressor, LightGBM, and XGBoost through simple averaging, achieving <span>(hbox {R}^{2})</span> = 0.949, RMSE = 214.8 kW, and MAE = 127.7 kW with only 142.4 seconds training time. Experimental validation on 4,213 observations demonstrates superior performance compared to individual models, positioning the system within 3.05% of the target 98% accuracy threshold while maintaining exceptional computational efficiency for real-time deployment.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-025-00604-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145930053","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

Substation drawing intelligent parsing framework with dense augmentation and semantic alignment 具有密集增强和语义对齐的变电站绘图智能解析框架

Energy Informatics Pub Date : 2025-12-29 DOI: 10.1186/s42162-025-00584-8

Tong Yan, Chaoyi Zhu, Xinhui Zhang, Yiheng Zeng

{"title":"Substation drawing intelligent parsing framework with dense augmentation and semantic alignment","authors":"Tong Yan, Chaoyi Zhu, Xinhui Zhang, Yiheng Zeng","doi":"10.1186/s42162-025-00584-8","DOIUrl":"10.1186/s42162-025-00584-8","url":null,"abstract":"<div><p>Substation engineering drawing parsing is essential for the automation, intelligence, and digital transformation of power systems. However, existing methods face significant challenges due to the complexity of these drawings and the limited availability of bitmap datasets. The drawings contain dense lines, specialized symbols, and intricate layouts, making it difficult for traditional object detection models to accurately identify components and text, often resulting in high rates of false positives and false negatives. Additionally, the lack of unified data standards leads to overfitting during model training, limiting generalization across diverse scenarios. To address these issues, we propose an integrated framework combining object detection and OCR for intelligent substation drawing analysis. Our method employs a dense random data augmentation matching strategy and an improved semantic alignment strategy to enhance feature robustness and model adaptability while maintaining computational efficiency. We also introduce a new dataset of 600 annotated substation engineering drawing images, covering various layout types and textual elements. Our experimental results show that our proposed method significantly outperforms existing techniques, achieving AP50, AP75, and mAP scores of 89.40%, 89.30%, and 63.00%, respectively. This demonstrates the effectiveness of our approach in accurately parsing complex substation drawings and contributes to the advancement of power systems’ automation and digital transformation.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-025-00584-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886938","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

EV-Insights: open source framework for electric vehicle charging data processing, analysis, and forecasting EV-Insights：电动汽车充电数据处理、分析和预测的开源框架

Energy Informatics Pub Date : 2025-12-17 DOI: 10.1186/s42162-025-00615-4

Marco Derboni, Matteo Salani

{"title":"EV-Insights: open source framework for electric vehicle charging data processing, analysis, and forecasting","authors":"Marco Derboni, Matteo Salani","doi":"10.1186/s42162-025-00615-4","DOIUrl":"10.1186/s42162-025-00615-4","url":null,"abstract":"<div><p>The rapid growth of electric vehicle adoption presents new challenges for distribution system operators and charging station owners. Distribution system operators must correctly manage charging demand, optimize infrastructure usage, and support grid stability, while charging station owners seek to analyze user behavior and predict future demand to improve operational efficiency. Meeting these goals requires accurate analysis and forecasting of charging behaviors. A major bottleneck, however, lies in the heterogeneity of available electric vehicle charging datasets: each dataset comes with its own structure, quality issues, and missing information, requiring time-consuming and error-prone preprocessing before any analysis or forecasting can be performed. To overcome this limitation, we introduce EV-Insights, an open-source framework designed to provide standardized services for data ingestion, preprocessing, analysis, and forecasting by supporting the integration of real-time data, synthetic data, and public datasets. Once data is integrated, users can easily generate insights on charging behavior and extend the framework with new analyses or forecasting models through modular interfaces. We evaluated EV-Insights using seven real-world public datasets comprising over 3 million charging sessions, demonstrating its potential to uncover valuable insights and support informed decision-making. Ev-Insights is available as open source at https://github.com/EV-Insights</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-025-00615-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145983028","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

Intelligent parameter recommendation for substation design using knowledge graph and graph neural networks 基于知识图和图神经网络的变电站设计参数智能推荐

Energy Informatics Pub Date : 2025-12-15 DOI: 10.1186/s42162-025-00605-6

Liang Zhou, Zhen-Ting Gao, Pei-Fan Zhai, Yi-Heng Zeng

{"title":"Intelligent parameter recommendation for substation design using knowledge graph and graph neural networks","authors":"Liang Zhou, Zhen-Ting Gao, Pei-Fan Zhai, Yi-Heng Zeng","doi":"10.1186/s42162-025-00605-6","DOIUrl":"10.1186/s42162-025-00605-6","url":null,"abstract":"<div><p>As power systems move toward digitalization and low-carbon transformation, improving the intelligence of substation design processes has become increasingly critical. Traditional equipment parameter selection relies heavily on manual experience and fragmented document retrieval, leading to inefficiencies, inconsistencies, and limited scalability. This paper proposes an intelligent parameter recommendation method tailored for substation engineering, integrating domain-specific knowledge graphs with adaptive graph neural networks (GNNs). The framework first extracts structured equipment information from multi-voltage substation design drawings using entity disambiguation, then constructs a hierarchical knowledge graph to represent inter-device relationships. A natural language interface captures user queries and encodes them into context-aware instruction vectors. These are used to guide a hybrid reasoning process that combines fuzzy rule matching and GNN-based relation inference. Case studies using real-world 10 kV/110 kV substation projects demonstrate that the proposed method significantly outperforms existing knowledge graph-based baselines in both accuracy and interpretability. The results show that this work is superior to the comparison baseline model in both ACC and AUC indicators, and support intelligent decision-making throughout the equipment lifecycle. This work provides a scalable solution for knowledge-driven substation design automation in the era of smart grids.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-025-00605-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146027091","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

Enhanced islanding detection using a hybrid machine learning approach 使用混合机器学习方法增强孤岛检测

Energy Informatics Pub Date : 2025-12-15 DOI: 10.1186/s42162-025-00607-4

Samiksha K. Shahade, Anjali U. Jawadekar, Aniket K. Shahade

引用次数: 0

Enhancing the accessibility of microgrid design tools for non-expert decision makers: a comparative usability evaluation of SAM and HOMER Grid 增强非专家决策者的微电网设计工具的可访问性：对SAM和HOMER网格的比较可用性评估

Energy Informatics Pub Date : 2025-12-13 DOI: 10.1186/s42162-025-00616-3

Michael Andrew Caballero, Murat Erkoc, Ramin Moghaddass

{"title":"Enhancing the accessibility of microgrid design tools for non-expert decision makers: a comparative usability evaluation of SAM and HOMER Grid","authors":"Michael Andrew Caballero, Murat Erkoc, Ramin Moghaddass","doi":"10.1186/s42162-025-00616-3","DOIUrl":"10.1186/s42162-025-00616-3","url":null,"abstract":"<div><p>Access to reliable and affordable energy is essential for manufacturing facilities, where even brief disruptions can result in significant financial losses. Solar-plus-storage microgrids offer a promising solution by enhancing energy resilience and reducing costs. However, the design and implementation of such systems remain challenging, especially for non-expert users such as building owners and facility managers. This study evaluates the usability and design effectiveness of two widely used microgrid planning tools, System Advisor Model (SAM) and HOMER Grid, through the lens of a novice user. Drawing on energy consumption data from a real manufacturing facility, two use cases are developed and modeled using both tools. The assessment framework is based on the IEEE Standard 1061 for Software Quality Metrics Methodology, emphasizing usability, system functionality, and component selection support. Both tools generate feasible system configurations but lack intuitive, step-by-step workflows and provide limited guidance on selecting photovoltaic modules, battery chemistries, and inverters. SAM provides more detailed modeling support, but its complexity can hinder adoption by non-technical users. We propose improvements to enhance accessibility, including guided design templates, time-aligned system sizing, prioritized input visualization, and integrated component recommendations. These enhancements could be implemented via SAM’s open-source development kit. The findings highlight the importance of usability and decision-support features in accelerating microgrid adoption, particularly for practitioners and facility managers without specialized expertise. Improving accessibility of these tools can directly support faster deployment of renewable microgrids, enhance resilience for commercial and industrial facilities, and contribute to broader decarbonization goals.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-025-00616-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026721","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 hybrid Stackelberg–Markov framework for adaptive load scheduling and dynamic pricing in smart grids 基于Stackelberg-Markov框架的智能电网自适应负荷调度与动态定价

Energy Informatics Pub Date : 2025-12-07 DOI: 10.1186/s42162-025-00614-5

Syed Ashraf Ali, Sohail Imran Saeed, Jehanzeb Khan, Shujaat Ali, Dilawar Shah, Muhammad Tahir

{"title":"A hybrid Stackelberg–Markov framework for adaptive load scheduling and dynamic pricing in smart grids","authors":"Syed Ashraf Ali, Sohail Imran Saeed, Jehanzeb Khan, Shujaat Ali, Dilawar Shah, Muhammad Tahir","doi":"10.1186/s42162-025-00614-5","DOIUrl":"10.1186/s42162-025-00614-5","url":null,"abstract":"<div><p>This paper proposes a Hybrid Stackelberg-Markov framework for adaptive load scheduling and dynamic pricing in smart grids. The framework integrates a Stackelberg game to model the interaction between the utility and consumers with a Markov process that captures consumer behavioral dynamics. By combining economic incentives with behavioral adaptation, the model achieves a balance between reducing the peak-to-average ratio (PAR), lowering consumer costs, and increasing utility profit. Simulation results demonstrate that the proposed approach reduces PAR by 43% compared with the baseline, decreases average consumer costs by 28%, and improves utility profit by 10%. The behavioral state analysis further shows that most consumers transition into the <i>Content</i> state, indicating long-term acceptance of dynamic pricing strategies. Moreover, the computational analysis confirms faster convergence and reduced run time compared with conventional demand response schemes. These results establish the proposed framework as a scalable and practical demand response solution for modern smart grids.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-025-00614-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982607","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

Federated data spaces for digital continuity in the building lifecycle: accelerating the mission of energy informatics 在建筑生命周期中实现数字连续性的联合数据空间：加速能源信息学的使命

Energy Informatics Pub Date : 2025-12-04 DOI: 10.1186/s42162-025-00610-9

Zheng Grace Ma, Bo Nørregaard Jørgensen

引用次数: 0

Multi-objective coordinated optimization of flexible interconnected distribution networks under rainstorm conditions 暴雨条件下柔性互联配电网多目标协调优化

Energy Informatics Pub Date : 2025-12-04 DOI: 10.1186/s42162-025-00612-7

Wei Zeng, Wei Pan, Guobing Liu, Jingguang Lu, Ke Wang, Chong Wang

{"title":"Multi-objective coordinated optimization of flexible interconnected distribution networks under rainstorm conditions","authors":"Wei Zeng, Wei Pan, Guobing Liu, Jingguang Lu, Ke Wang, Chong Wang","doi":"10.1186/s42162-025-00612-7","DOIUrl":"10.1186/s42162-025-00612-7","url":null,"abstract":"<div><p>The widespread integration of high-penetration distributed photovoltaic (PV) systems presents multiple challenges to distribution networks, particularly under extreme weather conditions such as short-term heavy rain, where issues like voltage violations and increased network losses become more severe. To address these challenges, this paper proposes a multi-objective coordinated optimization strategy for flexible interconnected distribution networks. The strategy establishes a multi-objective optimization model that comprehensively considers network losses, voltage deviations, PV output fluctuations, and regulation costs. It fully leverages the precise power flow regulation capability of the Soft Open Points (SOPs), coordinated with the fluctuation-smoothing effect of energy storage systems (ESS) and the voltage support function of multiple types of reactive power compensation devices. By introducing a special crowding distance (SCD) ordering mechanism, the traditional four-vector intelligent metaheuristic (FVIM) algorithm was upgraded to a multi-objective optimization algorithm for solution, significantly enhancing the quality of the Pareto optimal solution set. Simulation results based on the modified IEEE 33-bus and IEEE 39-bus systems show that the proposed strategy reduces the 24-hour total network loss by 19.17% and 6.37%, respectively, while keeping the minimum system voltage within the safe range throughout the day. Comparative analysis across multiple scenarios verifies the effectiveness and robustness of the strategy in smoothing PV fluctuations, optimizing system operation, and coping with extreme weather events. This research provides an efficient solution for the coordinated optimal operation of high-PV-penetration distribution networks, balancing economic efficiency, power quality, and operational stability.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-025-00612-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145930104","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

Operational optimization of high-proportion clean energy systems based on electricity-hydrogen-energy storage synergy 基于电-氢-储能协同的高比例清洁能源系统运行优化

Energy Informatics Pub Date : 2025-12-03 DOI: 10.1186/s42162-025-00611-8

Yangming Xiao, Jie Jiao, Wenwen Zhang, Wenshi Ren, Xiaobao Yu

{"title":"Operational optimization of high-proportion clean energy systems based on electricity-hydrogen-energy storage synergy","authors":"Yangming Xiao, Jie Jiao, Wenwen Zhang, Wenshi Ren, Xiaobao Yu","doi":"10.1186/s42162-025-00611-8","DOIUrl":"10.1186/s42162-025-00611-8","url":null,"abstract":"<div><p>High-renewable power systems are crucial for climate change mitigation and energy transition, yet their grid integration poses stability challenges. Hydrogen energy storage, with its large-scale and long-duration advantages, offers a promising solution to enhance flexibility against the volatility and intermittency of high-proportion clean energy. While recent studies have explored various electricity-synergy business models, they often lack a unified multidimensional evaluation framework. This study establishes an electricity-hydrogen-energy storage synergistic optimization model that minimizes total operational costs while comparing hydrogen transportation and electricity transmission modes. Simulation in a high-renewable demonstration area in Southwest China shows the hydrogen transportation mode outperforms, reducing wind and solar curtailment rates to 12.68% and 7.75%, respectively, cutting system costs by 57.3%, and generating additional hydrogen revenue. Sensitivity analysis identifies hydrogen selling price and production efficiency as key economic drivers, offering insights for planning and operating hydrogen storage in high-renewable systems. These findings support decision-making for electro-hydrogen system planning, business model innovation, and policy formulation.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-025-00611-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929992","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