Energy Informatics最新文献

{"title":"Energy informatics: a foundational discipline for the digital transformation of the energy sector","authors":"Bo Nørregaard Jørgensen,&nbsp;Zheng Grace Ma","doi":"10.1186/s42162-026-00677-y","DOIUrl":"10.1186/s42162-026-00677-y","url":null,"abstract":"<div><p>Energy systems are being reshaped by decarbonisation, decentralisation, electrification, sector coupling, digitalisation, and rising demands for resilience. These developments are changing energy systems from relatively centralised infrastructures into distributed cyber-physical systems that foster increasingly intelligent energy ecosystems. Almost a decade after the process of establishing the journal Energy Informatics began in 2017, the field has reached a level of thematic breadth, methodological maturity, and community identity that makes it timely to clarify its intellectual foundations. Energy Informatics has developed from an emerging research area concerned with digital applications in energy systems into a broader scientific and engineering discipline concerned with the information foundations of digital energy transformation. It is not defined by individual technologies such as data analytics, artificial intelligence, optimisation, smart meters, or digital twins, although these remain important parts of the field. It is concerned with how energy related information is represented, acquired, managed, exchanged, governed, analysed, and used to support the planning, operation, coordination, optimisation, and transformation of energy systems and energy ecosystems. Energy Informatics therefore provides the information centred perspective needed to connect energy engineering, information systems, computer science, software engineering, data science, artificial intelligence, cyber physical systems, energy economics, and sustainability transitions.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00677-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281719","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 cross-location evaluation of temporal fusion transformers and LSTM for long-term solar PV forecasting in arid environments","authors":"Hamza Zidoum,&nbsp;Haneen Ibrahim","doi":"10.1186/s42162-026-00672-3","DOIUrl":"10.1186/s42162-026-00672-3","url":null,"abstract":"<div><p>Long-term solar power forecasting at month- and year-ahead horizons is indispensable for grid investment planning, capacity adequacy assessment, and renewable energy policy in high-irradiance arid regions — yet deep learning architectures have not been systematically evaluated at these horizons across geographically diverse sites. This study addresses that gap by comparing Long Short-Term Memory (LSTM) networks and Temporal Fusion Transformers (TFT) for month-ahead (730-hour) and year-ahead (8,670-hour) photovoltaic (PV) power forecasting using three years of real on-site sensor data from ten grid-tied school PV systems (~ 130 kW each) spanning seven governorates of Oman. Thirty-five cross-location experiments evaluate generalisation to climatically unseen sites, providing a substantially more demanding benchmark than standard temporal train–test splits. Results reveal a fundamental accuracy–generalisability trade-off: Single-site LSTM achieves high month-ahead accuracy (best RMSE = 43.0 kW, R² = 0.989) at specific training-test pairs (Mudhaibi-trained on Yunqul). but degrades severely on transfer, while TFT trained on merged multi-site data outperforms LSTM by up to 57% in RMSE at specific sites (e.g., Duqum, Murbat), but underperforms at others (e.g., Mudhaibi, Ibri), highlighting site-dependent generalization. A novel architectural finding demonstrates that including Location ID as a feature causes LSTM cross-location performance to collapse (RMSE increase exceeding 195,000% in the extreme case; median degradation of 187% across sites), whereas TFT’s dedicated static covariate encoders leverage this information beneficially. TFT interpretability via variable selection networks and temporal attention identifies GHI and temperature as dominant predictors, reveals consistent 24-hour attention periodicity, and enables auditable model diagnostics critical for operational trust. Year-ahead TFT failed due to insufficient data at daily resolution (≤ 1,036 samples/site), suggesting larger datasets or alternative architectures may be needed; LSTM All-Locations models unexpectedly improve at this horizon. Evidence-based deployment guidelines are provided for grid operators selecting between site-dedicated LSTM and multi-site TFT approaches.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00672-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148837758","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 design of a fuzzy MPPT controller using an improved harris hawks optimization for photovoltaic systems","authors":"Tohid Hanafi,&nbsp;Ali Asghar Shojaei,&nbsp;Javad Mashayekhifard,&nbsp;Amin Honarbakhsh","doi":"10.1186/s42162-026-00675-0","DOIUrl":"10.1186/s42162-026-00675-0","url":null,"abstract":"<div><p>When maximum power point tracking (MPPT) is implemented in practical photovoltaic systems, rapid variations in irradiance and temperature, as well as partial shading conditions, can create multiple local maxima in the power–voltage curve and degrade the performance of conventional tracking methods. In this study, a fuzzy logic–based MPPT controller is adopted and its scaling factors and membership-function spread parameters are optimized using an improved Harris Hawks Optimization (I-HHO) algorithm to avoid subjective parameter tuning. In addition, two practical modifications are introduced: a soft dead zone to reduce steady-state ripple near the maximum power point and a lightweight global scanning mechanism with adaptive resolution to improve tracking under severe irradiance changes and partial shading. The proposed method is evaluated under several operating scenarios, including irradiance and temperature variations and partial shading, and compared with the conventional P&amp;O and INC methods. The results show that the multi-scenario objective function improves from 0.085267 in the baseline fuzzy controller to 0.071317 in the optimized version (about 16.36% improvement). In addition, the ripple in the temperature-step scenario decreases from 16.254% to 9.072%, while the settling time under partial shading improves from 0.078 s to 0.066 s. These results indicate that the proposed approach provides more stable tracking and improved dynamic performance for MPPT in practical operating conditions.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00675-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752387","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":"From models to meaning - an autoethnographic path toward a reflexive framework for interpreting human and ethical dimensions in energy system optimization","authors":"Chiara Bordin","doi":"10.1186/s42162-026-00674-1","DOIUrl":"10.1186/s42162-026-00674-1","url":null,"abstract":"<div><p>This study explores the ethical, social and interpretive aspects underlying the application of optimization to real-world decision-making, with special emphasis on energy-related applications. Based on ten years of interdisciplinary experience across academia, industry, and education, this research investigates how abstract optimization models gain meaning in practice within energy informatics applications such as electric vehicle charging, battery systems, energy system planning, and sustainable logistics. In this work, an autoethnographic approach is used to show that modeling is not only a technical activity, but also a highly interpretive process shaped by values such as efficiency, fairness, and accountability, as well as by the need to balance formal simplicity with complex stakeholder realities. Building on these insights the study proposes a reflexive framework that conceptualizes optimization as a meaning-making practice, with the objective of capturing how model outputs are constructed, interpreted, and negotiated across different contexts. The findings show that optimization models do not generate purely objective solutions. At the same time their outputs acquire meaning and authority through processes of framing, storytelling, and interaction with stakeholders. Ethical tensions, such as those between technical elegance and social responsibility, or between abstraction and contextual complexity, emerge as central features of modeling practice and are often managed through informal negotiation and compromise. This paper advances the understanding of optimization as a socially embedded practice, by introducing a structured framework grounded in empirical and reflexive analysis. It contributes to ongoing discussions in operations research and energy informatics by providing a conceptual lens for interpreting the human dimensions of modeling, and by supporting more reflective, context-aware, and ethically informed approaches to decision support.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00674-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148782284","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 short-term HVAC load forecasting via temporal feature engineering: insights from the PLEIAData building dataset","authors":"Seda Ermis","doi":"10.1186/s42162-026-00670-5","DOIUrl":"10.1186/s42162-026-00670-5","url":null,"abstract":"<div><p>Building energy consumption accounts for a significant portion of global energy use, with heating, ventilation, and air conditioning (HVAC) systems representing one of the largest contributors to building energy demand. Accurate short-term HVAC load forecasting is therefore essential for efficient building operation, demand-response strategies, and intelligent energy-management systems.This study investigates the impact of temporal feature representation on one-hour-ahead HVAC load forecasting using real operational data from the PLEIAData building dataset. Three forecasting models representing both machine learning and deep learning paradigms—Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—are evaluated under progressively enriched temporal feature configurations. The experiments systematically compare baseline environmental variables, autoregressive load features, rolling statistical representations, and extended temporal feature sets designed to capture recent HVAC operational dynamics.The results demonstrate that models relying only on contemporaneous environmental variables exhibit limited predictive capability, whereas incorporating temporal load-history information substantially improves forecasting accuracy. Extended temporal representations based on autoregressive and rolling statistical features consistently enhance model performance across all forecasting architectures. Among the evaluated configurations, XGBoost combined with the extended temporal feature representation achieves one of the best and most stable forecasting performances with an R² value of approximately 0.799. Additional ablation and interpretability analyses further show that recent temporal load information is the primary contributor to forecasting performance, while extending the feature space with increasingly complex rolling statistics and larger temporal representations provides only marginal additional gains. Overall, the study highlights the importance of temporal feature design, robustness-oriented evaluation, and interpretable forecasting analysis in HVAC load prediction applications.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00670-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751254","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 financial market equilibrium analysis based on a new energy trading model","authors":"Shuming Zhao","doi":"10.1186/s42162-026-00671-4","DOIUrl":"10.1186/s42162-026-00671-4","url":null,"abstract":"<div><p>The equilibrium of the energy financial market design for new energy trading models and the analysis of the energy financial market have become central challenges as electricity systems are rapidly transforming due to the integration of renewable energy. The expansion of renewable energy sources has created a structural dislocation between the physical dimensions of energy dispatch and financial risk management. Current energy trading models treat the physical and financial aspects as two independent optimisation problems and overlook the joint strategic behaviour that arises when participants operate with both operational exposure and financial hedge positions simultaneously. To address this gap, a co-optimised framework called the Dual-Layer Energy Trading Model (DLETM) is proposed in this paper, integrating both physical energy exchange and financial market participation. The physical layer controls the day-ahead flow of energy between renewable generators, prosumers, storage operators and retailers whereas the financial layer allows forward contracts, green energy certificates (GECs) and risk hedging instruments. The interconnection between layers is enforced by a Cross-Layer Volume Consistency Constraint (CLVCC) and coordinated by a Dual-Layer Pricing Signal (DLPS). Market equilibrium is formulated as a Generalized Nash Equilibrium Problem (GNEP) and reformulated as a Variational Inequality (VI). The existence of at least one Variational Equilibrium is established under standard convexity and compactness assumptions [1]. An Augmented Lagrangian Decomposition with Primal-Dual Coupling (ALD-PDC) algorithm is introduced for decentralised computation. Simulation experiments on a stylised market with 12 participants with 40% of renewable penetration indicate an improvement in social welfare of 8.3%, a reduction in physical price volatility of 21.7%, and an increase in renewable utilisation of 16.2% compared to single-layer benchmark models. Sensitivity analyses indicate the welfare benefit of the dual-layer architecture increases to 14.2% at 70% penetration, suggesting that this benefit grows with the progression of the energy transition.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00671-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148750728","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":"Scaling up repowering decisions: a farm-level financial decision support system for onshore wind energy assets","authors":"Yifan Wang,&nbsp;Maximilian Heumann,&nbsp;Tobias Kraschewski,&nbsp;Michael H. Breitner","doi":"10.1186/s42162-026-00668-z","DOIUrl":"10.1186/s42162-026-00668-z","url":null,"abstract":"<div><p>The aging of wind energy assets and the expiration of government subsidies make decisions on continued operation, repowering, and decommissioning increasingly critical for wind farm operators. Existing studies provide valuable insights into wind energy investment under uncertainty, but often provide limited consideration of turbine-level heterogeneity when analyzing decisions from a wind farm perspective. Following Design Science Research, this study proposes a Decision Support System (DSS) for wind farm operations that integrates several methodological components with distinct roles. Spatial and regulatory feasibility assessment identifies whether turbines and sites are eligible for repowering. Stochastic process simulation generates uncertain paths of electricity prices, turbine lifetimes, energy yields, and costs. Least Squares Monte Carlo-based real options valuation evaluates repowering timing for eligible turbines, and Monte Carlo-based net present value analysis assesses decommissioning timing for turbines without repowering eligibility. Building on these turbine-level valuations, the DSS applies a rule-based clustering approach to translate individual decision timings into batch-level coordinated strategies at the farm scale. The artifact is evaluated using a real wind farm in central Germany. The results indicate that, under the given assumptions, repowering-eligible turbines exhibit a pronounced early exercise window, whereas non-eligible turbines tend to be decommissioned at the end of their lifetimes. At the farm level, the system identifies three decision scenarios and compares their expected total returns. The batch-execution strategy, in which actions are carried out at each cluster’s timing, achieves the highest expected return in the case study. This study provides a reusable information-systems-based methodological approach and case-based evidence for coordinating repowering and decommissioning decisions in wind farms. The findings suggest that the proposed DSS can translate complex uncertainties into executable farm-level strategies and improve transparency in strategic decision-making.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00668-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148236866","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":"Application of big data in the network security situation awareness platform of intelligent power monitoring system","authors":"Liang Li,&nbsp;Jun Zhang,&nbsp;Peng Zhang,&nbsp;Liangjing Cao,&nbsp;Chenxiao Zhao","doi":"10.1186/s42162-025-00595-5","DOIUrl":"10.1186/s42162-025-00595-5","url":null,"abstract":"<div><h3>Introduction</h3><p>The growing complexity of network infrastructures has led to increasingly sophisticated network attacks. Traditional intrusion detection and firewall systems are no longer sufficient to handle these advanced threats. As a result, network situational awareness has emerged as a vital approach to enhance network security, particularly in power information systems. By leveraging big data technologies, network operators can better understand network behavior and perform rapid analyses of diverse data types, enabling effective threat detection and mitigation.</p><h3>Objectives</h3><p>The primary goals of this study are: To develop a robust security situational awareness framework for power information systems. To integrate tools and methods for efficient data collection, processing, storage, analysis, and visualization. To implement scalable and high-performance solutions for detecting and forecasting various network security risks.</p><h3>Methods</h3><p>Data Collection: Tools such as Flume and Kafka are used to collect network attack logs and relevant data.<i> Data Processing</i>: Frameworks like Apache Spark and Storm are employed for batch and real-time data analysis, ensuring fast and efficient handling of large datasets.<i> Feature Extraction</i>: A Deep Gated Recurrent Unit (D-GRU) model is used to determine index weights and extract critical features of the network security situation.<i> Scenario Assessment</i>: A decision matrix is constructed and analyzed using Apache Spark to assess the current state of network security.</p><h3>Results</h3><p>The proposed framework demonstrated scalability, ease of deployment, and high availability. These characteristics allowed for effective detection and forecasting of various network security threats, providing an advanced understanding of network behavior and enhancing overall security posture.</p><h3>Conclusion</h3><p>The integration of big data technologies with network situational awareness offers a significant improvement in detecting and preventing sophisticated network attacks. The proposed framework, which leverages tools like Flume, Kafka, Spark, and D-GRU, provides a comprehensive solution for network security, ensuring robust performance and adaptability to dynamic security challenges. This approach is particularly effective for power information systems, enabling proactive threat mitigation and improved network reliability.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-025-00595-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148750500","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":"MSBA-SAM: a multi-scale and boundary-aware framework for power grid segmentation in aerial image","authors":"Tao Guo,&nbsp;Kui Xu,&nbsp;Kailei Chen,&nbsp;Chun Xie,&nbsp;Shi Qiu,&nbsp;Rui Ye","doi":"10.1186/s42162-026-00669-y","DOIUrl":"10.1186/s42162-026-00669-y","url":null,"abstract":"<div><p>Addressing the issues of insufficient precision and difficulty in identifying small targets in deep learning-based aerial image power grid facility segmentation methods, this paper proposes an improved framework integrating multi-scale features and boundary awareness based on SAM. A dual-branch architecture combining ResNet-34 and ViT is constructed within the Image Encoder, balancing global structure capture and local detail extraction through channel-wise concatenation. An adaptive prompting mechanism is established leveraging the Feature Pyramid Network (FPN) and Adaptive Prompt Generator (APG), enhancing the automation and accuracy of power grid segmentation. A Boundary Awareness Module (BAM) and Channel-Spatial Hybrid Attention (CBAM) are embedded in the Mask Decoder, paired with a cross-entropy-edge hybrid loss function that strengthens the boundary segmentation accuracy of small targets by constraining both boundary precision and recall. Ablation studies and comparative experiments with different algorithms validate the effectiveness and advancement of the proposed enhanced SAM model for aerial image power grid segmentation tasks.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00669-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148614440","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 early warning of substation equipment based on GNN network and digital twin","authors":"Luyao Pei,&nbsp;Weican Yuan,&nbsp;Cheng Xu,&nbsp;Chentao Wang","doi":"10.1186/s42162-026-00665-2","DOIUrl":"10.1186/s42162-026-00665-2","url":null,"abstract":"<div><p>To solve the problems of insufficient real-time performance, weak adaptability to coupling faults and poor reliability of traditional early warning methods for substation equipment, this study proposes an intelligent early warning model (GNN-DT) which integrates Graph Neural Network (GNN) and Digital Twin (DT). In this model, the substation system is abstracted as a weighted undirected graph, the physical model of key equipment is constructed through DT, and the parameters are adaptively modified by multi-source data fusion. a Graph Attention Mechanism (GAT) based on electrical characteristics is designed, the coupling relationship between devices and extract weak abnormal features is strengthened. With the help of Multi-Task Learning (MTL) framework with physical consistency constraints, the dynamic hierarchical early warning from device level to system level is completed. The experiment is verified by simulation data and real operation data. Experimental results show that the model achieves over 93% across four core metrics: accuracy, precision, recall, and F1-score. The false positive rate is as low as 3.5%, and the average early warning time is only 4.2 min, which meets the engineering requirement of warning response time (&lt; 100 ms) for real-time monitoring systems in substations. Its overall performance is significantly superior to traditional machine learning, single deep learning, and single GNN/DT models. The above evaluation indicators comprehensively verify the superiority of the model from three dimensions: identification accuracy (accuracy, precision, recall, F1-score), reliability (false positive rate), and response timeliness (average early warning time). The model shows strong robustness in 10 typical fault scenarios, the recall rate of hidden faults is over 89%, and it still has excellent generalization ability in data scarce scenarios. The single sample inference time of 4.0ms can meet the requirements of engineering real-time deployment. This study realizes the organic integration of data-driven and mechanism-driven, provides reliable technical solutions for intelligent operation and maintenance of substations. The study is of great significance to improving the safe and efficient operation level of power grids.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00665-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465642","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}