Energy Informatics最新文献

{"title":"A data-driven framework for estimating remaining driving range in cargo electric vehicles","authors":"Mrugank Gandhi,&nbsp;Archana Y. Chaudhari,&nbsp;Rahesha Mulla","doi":"10.1186/s42162-026-00618-9","DOIUrl":"10.1186/s42162-026-00618-9","url":null,"abstract":"<div>\u0000 \u0000 <p>The increasing shift to sustainable transportation has fueled growing interest in electric vehicles, including the key issue of estimating the remaining driving range with high precision. The study develops a data-driven approach to predict the cargo electric vehicle’s remaining driving range that incorporates machine learning based estimation. Real-world operational data: a Musoshi Pop-Up Mini electric cargo vehicle was tested under various load and speed characteristics on a 2 km campus route, providing a possibility of high-resolution modeling of energy consumption patterns. After systematic preprocessing with feature engineering and segment-wise aggregation, seven regression algorithms: ElasticNet, Support Vector Regression, Random Forest, LightGBM, XGBoost, CatBoost, and ExtraTrees were optimized with Optuna-based Bayesian hyperparameter tuning and exhaustively compared in terms of RMSE, MAE, and R². Amongst these, the SVR model RMSE equal to 2.37, MAE equal to 1.75, and R² equal to 0.892 demonstrated the best performance and outperformed other ensemble and gradient boosting models. The obtained results prove that data-driven models, can reliably assess energy consumption and range for cargo EVs, which would ensure the safer and more reliable deployment of electric mobility systems.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00618-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147338772","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":"Harnessing AI in renewable energy systems: driving environmental and socio-economic transformation","authors":"M. M. Mundu,&nbsp;Mariam Basajja,&nbsp;Emmanuel Kweyu,&nbsp;V. S. Manjula,&nbsp;Daniel Ejim Uti","doi":"10.1186/s42162-026-00624-x","DOIUrl":"10.1186/s42162-026-00624-x","url":null,"abstract":"<div>\u0000 \u0000 <p>The rapid expansion of renewable energy systems has intensified interest in Artificial Intelligence (AI) for improving forecasting, optimisation, and operational reliability. Yet despite growing technical advances, existing reviews focus narrowly on algorithmic performance and rarely integrate environmental, socio-economic, and governance dimensions, issues that are especially critical for low- and middle-income countries (LMICs), where data scarcity and institutional capacity constraints shape deployment outcomes. This creates an incomplete understanding of the opportunities and risks associated with AI-enabled renewable energy transitions. This study conducts a systematic review, following PRISMA guidelines, to synthesise evidence across four domains: technical integration, environmental impacts, socio-economic implications, and governance considerations. The review examines 113 studies spanning solar, wind, microgrids, storage management, and predictive maintenance. Findings show that while AI can enhance forecasting accuracy and system efficiency, these benefits are highly context-dependent and often derived from simulations rather than field deployments. The literature reveals underexplored risks, including the computational energy footprint of AI models, limited transferability to data-scarce regions, potential reinforcement of inequality in LMIC, and increasing concentration of technological power in corporate actors. Based on these, the paper proposes a cross-sectoral framework for responsible AI adoption in renewable energy and outlines priority actions for researchers, policymakers, and practitioners. These include rigorous reporting of model uncertainty and lifecycle impacts, strengthening data governance and local capacity, and validating AI tools in real-world low-resource contexts. The review concludes that AI can support sustainable energy transitions only when deployed within robust technical, institutional, and equity-oriented governance systems.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00624-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147338101","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":"Integrated green smart grids for high-speed rail transit systems","authors":"Nisha Prasad,&nbsp;Mahipal Bukya","doi":"10.1186/s42162-026-00626-9","DOIUrl":"10.1186/s42162-026-00626-9","url":null,"abstract":"<div>\u0000 \u0000 <p>The integration of green technologies in smart grids is changing the concept of global energy systems. It is not only improving energy efficiency and reducing carbon footprints but also enhancing grid sustainability. They include integrated renewable energy resources (RES), such as wind and solar, distributed energy resources (DER), and functionalities such as energy storage (EST), optimization using artificial intelligence (AI), smart metering, and load management. Adoption of these technologies not only changes the global supply system but also affects the operation of high-speed railways (HSR) massively. Fast adoption of smart technologies raises an alarming concern of cyber security in HSR systems. In view of increasing energy demands and specific operational constraints of HSR systems, this paper provides a comprehensive review of these green technologies and their applicability. It examines the implementation of these technologies, highlighting their role in enhancing the resilience and sustainability of HSR supply systems. Furthermore, the review explores the key technical challenges associated with integration, such as RES intermittency and cybersecurity vulnerabilities specific to rail infrastructure. This review synthesizes key developments and practical applications in the rail sector. This paper provides a valuable resource for researchers interested in developing cleaner and resilient transport systems.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00626-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147337330","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":"Physics-informed voting ensemble for solar power generation forecasting: integrating domain knowledge with machine learning","authors":"Manimaran Naghapushanam,&nbsp;Baskaran Jeevarathinam,&nbsp;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}

{"title":"Substation drawing intelligent parsing framework with dense augmentation and semantic alignment","authors":"Tong Yan,&nbsp;Chaoyi Zhu,&nbsp;Xinhui Zhang,&nbsp;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}

{"title":"EV-Insights: open source framework for electric vehicle charging data processing, analysis, and forecasting","authors":"Marco Derboni,&nbsp;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}

{"title":"Intelligent parameter recommendation for substation design using knowledge graph and graph neural networks","authors":"Liang Zhou,&nbsp;Zhen-Ting Gao,&nbsp;Pei-Fan Zhai,&nbsp;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}

{"title":"Enhanced islanding detection using a hybrid machine learning approach","authors":"Samiksha K. Shahade,&nbsp;Anjali U. Jawadekar,&nbsp;Aniket K. Shahade","doi":"10.1186/s42162-025-00607-4","DOIUrl":"10.1186/s42162-025-00607-4","url":null,"abstract":"<div><p>This paper presents a novel hybrid machine learning method for enhanced islanding detection in distributed generation systems. The proposed approach integrates distinct feature extraction from voltage and current signals at the point of common coupling with an optimized Extreme Gradient Boosting (XGBoost) classifier to accurately differentiate islanding events from normal grid disturbances. Validated on a public residential microgrid dataset, the method demonstrates superior performance by achieving a high detection accuracy of 97.83%, effectively eliminating the non-detection zone, and maintaining a detection time of under two cycles. This approach provides a robust, non-intrusive, and computationally efficient solution for anti-islanding protection, significantly outperforming conventional passive techniques, while the use of a publicly available dataset ensures full reproducibility and offers a benchmark for future research.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 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/s42162-025-00607-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778746","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":"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,&nbsp;Murat Erkoc,&nbsp;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}

{"title":"A hybrid Stackelberg–Markov framework for adaptive load scheduling and dynamic pricing in smart grids","authors":"Syed Ashraf Ali,&nbsp;Sohail Imran Saeed,&nbsp;Jehanzeb Khan,&nbsp;Shujaat Ali,&nbsp;Dilawar Shah,&nbsp;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}