Energy Informatics最新文献

{"title":"A day-ahead and intraday scheduling method for polysilicon industrial parks with wind power integration based on flexible hydrogen production coupling","authors":"Yulong Yang,&nbsp;Chunye Qu,&nbsp;Songnan Wang,&nbsp;Jianwu Cai,&nbsp;Yaodong Gong","doi":"10.1186/s42162-026-00633-w","DOIUrl":"10.1186/s42162-026-00633-w","url":null,"abstract":"<div>\u0000 \u0000 <p>The increasing penetration of renewable energy introduces significant uncertainty to the operation of energy-intensive industries. To address this challenge, we propose a multi-timescale scheduling model for wind power integration in polysilicon industrial parks. The model consists of a day-ahead component, which minimizes operational costs by coordinating polysilicon reduction and water electrolysis hydrogen production while allocating reserve capacity. A chance-constrained programming approach is employed to determine reserve capacity, ensuring a balance between reliability and economic efficiency. The intraday component, benefiting from improved wind power forecasting accuracy, engages only the water electrolysis process to absorb excess renewable generation. By involving water electrolysis in both timescales, the model enhances operational flexibility and renewable utilization. Case studies demonstrate the effectiveness of the proposed approach, highlighting its potential to support low-carbon and cost-efficient operation in polysilicon industrial parks.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00633-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147341762","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":"Contrastive-learning-based wireless communication link quality assessment model for grids","authors":"Mei Ma,&nbsp;Huan Xie,&nbsp;Xing Li,&nbsp;Xueting Fan,&nbsp;Weifu Peng,&nbsp;Xuxu Li","doi":"10.1186/s42162-026-00622-z","DOIUrl":"10.1186/s42162-026-00622-z","url":null,"abstract":"<div><p>The rapid advancement of smart grids necessitates robust dynamic assessment of wireless communication link quality, which faces dual challenges: complex electromagnetic interference (EMI) and the need for effective multi-source temporal data correlation modeling. Traditional methods relying on manual expertise and existing data-driven models often inadequately capture intricate multi-source temporal features. To address these limitations, this paper proposes a novel contrastive learning-based model for wireless link quality assessment in smart grids. Our framework employs Link Quality Indicator (LQI), Received Signal Strength Indicator (RSSI), and Signal-to-Noise Ratio (SNR) as multi-view inputs. A cross-view semantic alignment strategy is introduced to extract noise-robust shared features across these heterogeneous indicators. Furthermore, we design a hybrid attention temporal encoder integrating Long Short-Term Memory (LSTM) networks, adaptive channel attention, and global temporal attention modules. This cascaded architecture achieves deep fusion of local dynamic feature enhancement and global long-range dependency modeling. Experimental validation on 48 hours of continuously collected real-world communication link data demonstrates that the proposed model outperforms baseline methods, achieving accuracy improvements of 2.5% to 7.7% with validated statistical significance. Specifically, for abnormal link states, the model maintains a high recall rate of over 92.1%, ensuring reliable fault detection. While maintaining high overall stability, we observe minor performance degradation under conditions of extreme burst noise or high rates of missing data. Crucially, it exhibits substantially enhanced robustness and generalization capability, particularly in identifying abnormal link states under challenging EMI conditions.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00622-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146027113","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 method for short-term photovoltaic power prediction integrating long short-term memory network, differential transformer, and multi-objective escape algorithm","authors":"Yi Zhang,&nbsp;Guangde Zhang,&nbsp;Zengwei Li,&nbsp;Hongkai Zhao,&nbsp;Yuanming Ma,&nbsp;Guodong Li,&nbsp;Rongfu Zhang","doi":"10.1186/s42162-026-00621-0","DOIUrl":"10.1186/s42162-026-00621-0","url":null,"abstract":"<div>\u0000 \u0000 <p>With the rapid development of renewable energy, photovoltaic power generation has become a key part of the global energy transition. Short-term photovoltaic prediction is critical for intra-day real-time power grid dispatching, and enhancing its accuracy is a key research focus. However, existing methods still have limitations in handling complex nonlinear relationships in photovoltaic temporal data. To tackle this, this paper proposes a new model combining Long Short-Term Memory (LSTM), Differential Transformer (DiffTransformer), and Multi-Objective Escape Algorithm (MOESC) for short-term photovoltaic power prediction optimization: Preprocessed data is input into the LSTM-Differential Transformer model, with the Differential Transformer encoder capturing fine-grained temporal changes via optimized multi-head attention and rotary positional encoding, and the LSTM decoder integrating local temporal information for power prediction. Subsequently, Pareto-improved MOESC performs multi-objective optimization on the model’s key parameters (balancing <i>RMSE</i>, <i>MAE</i>, and <i>R²</i>), with the optimal parameters selected from the Pareto frontier. Experiments based on the Guoneng Rixin photovoltaic dataset show that, with user-defined weights (<i>RMSE</i>: 30%, <i>MAE</i>: 30%, <i>R²</i>: 40%), this method outperforms XGBoost, LightGBM, SVR, LSTM, GRU and the unoptimized LSTM-Differential Transformer model in photovoltaic power prediction. It not only can effectively improve prediction accuracy but also exhibits better stability compared with the unoptimized LSTM-Differential Transformer model.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00621-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147340599","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 SVMD-RIME-TCN-BiGRU model for wind power prediction","authors":"Kaikai Gu,&nbsp;Lei Cao,&nbsp;Jing Cao,&nbsp;Mu LI,&nbsp;Hanchao Chen,&nbsp;Zhong Wang,&nbsp;Sheng Liu,&nbsp;Kefei Zhang","doi":"10.1186/s42162-026-00630-z","DOIUrl":"10.1186/s42162-026-00630-z","url":null,"abstract":"<div><p>Accurate short-term wind power prediction (WPP) is critical for power system stability but remains challenging due to the inherent non-linearity and volatility of wind series. This study proposes a novel framework, SVMD-RIME-TCN-BiGRU, to address these challenges. First, the Maximal Information Coefficient (MIC) is used to select high-correlation features and eliminate redundancy. Second, Successive Variational Mode Decomposition (SVMD) decomposes raw data into successive intrinsic modes, effectively mitigating non-stationarity and avoiding the mode-mixing issues of traditional methods. Third, a hybrid Temporal Convolutional Network-Bidirectional Gated Recurrent Unit (TCN-BiGRU) model is constructed to extract spatiotemporal features. Crucially, the RIME optimization algorithm is introduced to automatically tune the key hyperparameters of the TCN-BiGRU, avoiding local optima. Experimental results on a Xinjiang wind farm dataset demonstrate that the proposed model achieves a Root Mean Square Error (RMSE) of 7.6882 and an R² of 0.9813. It significantly outperforms baseline models (including LSTM, TCN, and Transformer) and other hybrid variants, reducing errors by over 38% compared to the TCN-BiGRU baseline. This validates the framework’s reliability and accuracy for practical power dispatching.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00630-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026981","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":"Design principles and experimental analysis of secure data exchange approaches for distributed cyber-physical sensors in electric grid systems","authors":"Patricia Cordeiro,&nbsp;Shamina Hossain-McKenzie,&nbsp;Adam Summers,&nbsp;Adrian Chavez,&nbsp;Georgios Fragkos,&nbsp;Khandaker Akramul Haque,&nbsp;Mohamed Massoudi,&nbsp;Alex Reyna,&nbsp;Taylor Collins,&nbsp;Katherine Davis","doi":"10.1186/s42162-026-00617-w","DOIUrl":"10.1186/s42162-026-00617-w","url":null,"abstract":"<div><p>Critical infrastructure systems such as the electric grid are increasingly cyber-physical, where communication and control are tightly intertwined with the physics-based processes of power flow. To ensure safe and resilient operation of these cyber-physical systems, a variety of sensors and analyses are required for monitoring and detection of abnormal or malicious behavior to achieve full cyber-physical situational awareness (CPSA). To share this collected data with different analysis platforms, whether intrusion detection systems or state estimation algorithms, secure data exchange is essential. Designing secure data exchange across interconnected systems of systems (SoS) can be challenging without considering unique characteristics of the underlying cyber and physical processes. It is important to consider different types of communication protocols, frequency of communications, and types of communications (e.g., sensor measurements, control commands). In this paper, we provide design principles and experimental results for secure and resilient data exchange across distributed sensors and analytics in decentralized, cyber-physical energy systems. Specifically, secure data exchange technologies such as IPFS, synchronic web, multichain, and storage/sharing principles are presented and experimental results are provided to assess their applicability to exemplar distributed CPSA sensors.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00617-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147339715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The drivers of energy efficiency in emerging economies: do financial inclusion, fintech and foreign direct investment matter?","authors":"Shnehal Soni,&nbsp;R. L. Manogna","doi":"10.1186/s42162-026-00628-7","DOIUrl":"10.1186/s42162-026-00628-7","url":null,"abstract":"<div><p>The study examines the impact of financial inclusion, fintech and foreign direct investment (FDI) on energy efficiency during 2004–2022 for ten emerging economies which include Brazil, China, Russia, South Africa, India, Malaysia, Indonesia, Thailand, Mexico and Turkey. Fixed effect ordinary least squares (FEOLS), fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) techniques were applied to quantify the relationship among the variables in long run. Findings suggest that financial inclusion, fintech and FDI are important factors driving energy efficiency. Financial inclusion provides opportunities toinvestors to invest in energy-efficient technologies at a reduced cost. Availability of infrastructure for fintech is shown to have a favorable impact on energy efficiency. Therefore, investment in digital infrastructure should be prioritized which will increase the availability of fintech services. Policymakers should also take steps to channelize FDI inflows into research and development (R&amp;D) which would help in developing energy efficient technologies.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00628-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147339216","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 photovoltaic–diesel–battery hybrid renewable energy system for sustainable off-grid electrification of a village in Ethiopia","authors":"Yonas Tibebu Mekonnen,&nbsp;Endalkachew Addis Mekonnen,&nbsp;Haiter Lenin Allasi,&nbsp;Sujin Jose Arul,&nbsp;Mary Vasanthi Soosaimariyan","doi":"10.1186/s42162-026-00625-w","DOIUrl":"10.1186/s42162-026-00625-w","url":null,"abstract":"<div>\u0000 \u0000 <p>Rural electrification in developing regions remains a major challenge due to the absence of grid infrastructure and high costs of energy distribution. This study presents the design, simulation, and optimization of a hybrid photovoltaic (PV)–diesel–battery system to supply reliable electricity to an off-grid village in the Jawi region of Ethiopia. With an annual load demand of 153,396.36 kWh and average solar radiation of 1211.8 kWh/m²/year, a hybrid configuration comprising an 81 kW PV array, 25 kW diesel generator, 45 kW converter, and 200 batteries was modeled using PVsyst and HOMER software. Various system configurations were evaluated based on net present cost (NPC), cost of energy (COE), fuel consumption, and CO₂ emissions. The optimized hybrid system achieved a net present cost of 7,284,233 Birr, a cost of energy of 4.468 Birr/kWh, annual diesel consumption of 9,377 L, and CO₂ emissions of 24,693 kg, with a renewable contribution of 84%. Compared to the diesel-only configuration, the hybrid system significantly reduced both fuel usage and emissions while improving reliability and long-term sustainability. Sensitivity analyses confirmed system robustness against variations in fuel price and PV cost. The results demonstrate that a PV–diesel–battery hybrid system offers a technically feasible, economically viable, and environmentally sustainable solution for off-grid electrification in rural Ethiopia.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00625-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147338792","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 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}