Energy Informatics最新文献

{"title":"Intelligent guarantee power supply decision method based on reinforcement learning algorithm","authors":"Milu Zhou,&nbsp;Huijie Sun,&nbsp;Tian Yang,&nbsp;Tingting Li,&nbsp;Qi Hou","doi":"10.1186/s42162-025-00535-3","DOIUrl":"10.1186/s42162-025-00535-3","url":null,"abstract":"<div><p>Traditional power supply decision methods rely on fixed and rigorous mathematical models, which are difficult to accurately capture the characteristics and changing patterns of new loads, resulting in low prediction accuracy. Therefore, a decision model for guaranteeing power supply is constructed based on an improved proximal policy optimization algorithm, to study the intelligent guarantee power supply decision method. The experimental results show that the stability of the proximal policy optimization algorithms is generally high in all scenarios, especially in fault or anomaly scenarios and low load demand scenarios, which exceeds 110%. Its loss value decreases with the increase of training iterations. At 60 iterations, its loss value reaches the optimal value of 100 and then tends to stabilize. The research results indicate that the intelligent power supply strategy has good feasibility. This decision method helps to improve the stability, efficiency, intelligence level, and ability to respond to emergencies of the power grid.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00535-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145143036","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 rapid Multi-source information integration method based on improved federated learning for a “dual carbon” smart monitoring center in a Dual-carbon context","authors":"Jia Liu,&nbsp;Zhenhua Yan,&nbsp;Liang Wang,&nbsp;Wenni Kang,&nbsp;Jiangbo Sha","doi":"10.1186/s42162-025-00537-1","DOIUrl":"10.1186/s42162-025-00537-1","url":null,"abstract":"<div><p>To achieve the rapid unsupervised learning of multi-source information, this paper studies a multi-source information integration method for the “dual carbon” smart monitoring center based on the improved federated learning. To solve the problem of rapid integration information from many sources in the “dual carbon” smart monitoring center, a multimodal federated learning framework is built on the basis of the traditional federated learning. The generator and discriminator of the conditional generative adversarial network model are used to distinguish between the generated pseudo-samples and normal samples, and the multi-source information is obtained unsupervisedly. Based on the global distribution, the fast integration is achieved by using the passive distillation method of federated data. At the same time, the stochastic gradient descent is used to enhance the learning rate, improve the learning ability of the model, and promote the unsupervised fast fusion. The experiment shows that this method can effectively integrate the multi-source information, display the spatial status of carbon emissions and enterprise energy production data. The integrated information has high completeness and entropy value, and is accurate and applicable in the multi-source information integration of the “dual carbon” smart monitoring center.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00537-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145142691","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":"Construction of health status recognition and prediction model for power communication equipment based on TFIDF-COS","authors":"Jianliang Zhang,&nbsp;Yang Li,&nbsp;Junwei Ma,&nbsp;Xiaowei Hao,&nbsp;Chengpeng Yang,&nbsp;Meiru Huo,&nbsp;Sheng Bi,&nbsp;Zhifang Wen","doi":"10.1186/s42162-025-00532-6","DOIUrl":"10.1186/s42162-025-00532-6","url":null,"abstract":"<div><p>When power communication equipment malfunctions, the stability and safety of the power grid are compromised. This is due to the health status of the equipment. The safety and stability of the power grid will be impacted if dispatchers take too long to identify the fault’s origin and kind, which will disrupt the power communication system’s regular operations. In order to solve the problem of poor health status of power communication system due to the inability to timely determine and deal with the faults of power communication equipment, the study proposes the construction of health status recognition of power communication equipment with prediction model based on term frequency-inverse document frequency and cosine similarity. The model firstly extracted the fault information of power communication equipment and builds the fault knowledge graph. Secondly, the study identified and built a prediction model for the health status of power communication equipment based on term frequency-inverse document frequency and cosine similarity model. The outcomes revealed that the training model had the highest accuracy and the lowest loss rate when the learning rate was set to 1 × 10⁻⁵. When the iterations was set to 70, the training and test sets had the highest accuracy and the lowest loss rate. When the model utilized in the study was compared to other models with varying numbers of samples in the dataset, it performed well in terms of runtime and fault diagnosis accuracy. The model developed by the study improves the accuracy of fault extraction and recognition and can better ensure the normal operation of power communication equipment.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00532-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145142357","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":"Information and Communication Technologies (ICT) for the intelligent operation of building energy systems: design, implementation and evaluation in a living lab","authors":"Florian Redder,&nbsp;Philipp Althaus,&nbsp;Eziama Ubachukwu,&nbsp;Maximilian Mork,&nbsp;Sascha Johnen,&nbsp;Christian Küpper,&nbsp;Paul Lieberenz,&nbsp;Marieluise Oden,&nbsp;Lidia Westphal,&nbsp;Thomas Storek,&nbsp;André Xhonneux,&nbsp;Dirk Müller","doi":"10.1186/s42162-025-00536-2","DOIUrl":"10.1186/s42162-025-00536-2","url":null,"abstract":"<div><p>Successful adaptation to climate change requires resilient, reliable, and efficient energy systems. To unlock energy efficiency potentials in buildings, an intelligent, user-centered approach is vital. However, this requires handling diverse data on the energy system. Therefore, technologies for harmonizing, storing, and visualizing data, as well as managing physical devices and users are needed. This work assesses existing and required Information and Communication Technologies (ICT) for intelligent building energy system operation. We propose an intermediate architecture based on Internet of Things (IoT) core principles and feature insights from its implementation within the Living Lab Energy Campus (LLEC) at Forschungszentrum Jülich. We present an approach for integrating existing ICT components, such as building energy metering and central Heating, Ventilation and Air Conditioning (HVAC) management, and propose a comprehensive data collection and distribution infrastructure. We establish IoT-enabled applications for energy system monitoring, user engagement, advanced building operation, and device identification and management. We evaluate our ICT setup through functional and performance assessments. We find that heterogeneous data can be reliably collected, distributed, and managed using standardized interfaces, state-of-the-art databases, and cutting-edge software components. For the buildings operated through the ICT infrastructure, data transmission availability is above 98.90 %, mean time to repair (MTTR) is less than 2.68 h, and mean time between failures (MTBF) is in the range of 242.67 h to 1092.00 h, evaluated over a period of three months. Our approach promotes the early real-world adoption of intelligent building control prototypes and their sustainable development. We demonstrate the proposed ICT setup through an experimental study that applies a cloud-based Model Predictive Controller (MPC) to a real building space. Our results provide a comprehensive discussion of the required ICT setup for intelligent building energy system control in real-world environments, and highlight important design strategies that reduce the conceptual overhead and facilitate implementation in similar projects.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00536-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145142004","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":"AI in power systems: a systematic review of key matters of concern","authors":"Felipe Henao,&nbsp;Robert Edgell,&nbsp;Ambar Sharma,&nbsp;Jeffrey Olney","doi":"10.1186/s42162-025-00529-1","DOIUrl":"10.1186/s42162-025-00529-1","url":null,"abstract":"<div><p>Recent advances in Artificial Intelligence (AI) have generated both excitement and concern within the power sector. While AI holds significant promise, enabling improved forecasting of renewable energy generation, enhanced grid resilience, and better supply-demand balancing, it also raises critical issues around transparency, data privacy, accountability, and fairness in power distribution. Despite the growing body of research on AI applications in power systems, there is a lack of structured understanding of the key socio-technical matters of concern (MCs) surrounding its integration. This paper addresses this gap by conducting a <i>systematic literature review combined with qualitative text analysis</i> to identify and synthesize the most prominent socio-technical concerns in the academic discourse. We analyzed a curated sample of peer-reviewed papers published between 1987 and 2024, focusing on high-impact journals in the field. Our analysis reveals four major categories of concern: (1) <i>Operational Concerns</i>-relating to AI’s reliability, efficiency, and integration with existing grid systems; (2) <i>Sustainability Concerns</i>-centered on energy consumption, environmental impact, and AI’s role in the energy transition; (3) <i>Trust Concerns</i>-including transparency, explainability, cybersecurity, and ethics; and (4) <i>Regulatory and Economic Concerns</i>-covering issues of accountability, regulatory compliance, and cost-effectiveness. By mapping these concerns into a cohesive analytical framework, this study contributes to the literature by offering a clearer understanding of AI’s sociotechnical challenges in the power sector. The framework also informs future research and policymaking efforts aimed at the responsible and sustainable deployment of AI in power systems.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00529-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145142143","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":"Synchronous phasor anomaly detection method of real-time electricity price data in power market considering recording deviation","authors":"Xin Zhao,&nbsp;Zhe Liu,&nbsp;Meng He","doi":"10.1186/s42162-025-00534-4","DOIUrl":"10.1186/s42162-025-00534-4","url":null,"abstract":"<div><p>In the bilateral negotiated electricity market, the existence of abnormal data in the real-time node electricity prices poses a great threat to the stability and reliability of the power system. This paper introduces a synchronous phasor anomaly detection method considering the influence of recording deviation. This paper comprehensively analyzes the causes of abnormal real-time node electricity price data collected by PMU in the bilateral negotiated electricity market. A weighted time–frequency transform is proposed. The estimator achieves the accurate synchronous phasor measurement of real-time node electricity price data by creatively combining the frequency discretization and online signal frequency detection technology. It also successfully minimizes the interference of recording deviation on synchronous phasor measurement. Comparing the estimated value of the SCADA system with the measured value of the PMU is part of the anomaly detection process.The experimental results prove the effectiveness of this method. It achieves a high level of accuracy and minimum error when processing the real-time node electricity price data. This method can accurately identify various anomalies, such as those related to node voltage, phase angle and power. In addition, this method has high detection accuracy and greatly improves the reliability of power system anomaly detection. This method not only provides reliable data for transaction decisions and operational evaluations in the power market but also enhances the power system’s safety and stability through timely detection of potential issues.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00534-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145145271","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":"Correction: Day-ahead photovoltaic power forecasting with multi-source temporal-feature convolutional networks","authors":"Ziming Ouyang,&nbsp;Zhaohui Li,&nbsp;Xiangdong Chen","doi":"10.1186/s42162-025-00539-z","DOIUrl":"10.1186/s42162-025-00539-z","url":null,"abstract":"","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00539-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145144866","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 development of an intelligent comprehensive detection instrument for circuit breakers in power systems and its key technologies","authors":"Weimin Guan,&nbsp;Han Hu,&nbsp;Chao Sun,&nbsp;Jie Ji","doi":"10.1186/s42162-025-00497-6","DOIUrl":"10.1186/s42162-025-00497-6","url":null,"abstract":"<div><p>To improve the accuracy and reliability of circuit breaker detection in power systems, this study proposes an intelligent detection instrument. The instrument addresses key issues found in traditional methods, such as limited real-time performance, inadequate data integration capabilities, and poor environmental adaptability. The instrument integrates multimodal data fusion technology to comprehensively analyze electrical parameters, mechanical characteristics, and environmental factors, enabling full awareness of the circuit breaker’s status. Additionally, this study optimizes the fault diagnosis algorithm, enhancing detection stability and robustness. By improving the model architecture, the computational burden is reduced, making the system more suitable for real-time monitoring and resource-constrained environments. Experimental results demonstrate that the intelligent detection instrument outperforms existing methods in terms of accuracy, detection efficiency, and anti-interference capabilities. It can more effectively identify the operational status of circuit breakers while maintaining high detection performance under complex operating conditions. Compared to traditional methods, the proposed solution shows significant advantages in reducing false alarms, optimizing detection speed, and improving environmental adaptability. Therefore, the study provides efficient and stable technical support for intelligent circuit breaker detection in power systems, laying a solid foundation for the development of smart grids.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00497-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144140190","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":"Research on load frequency control system attack detection method based on multi-model fusion","authors":"Feng Zheng,&nbsp;Weixun Li,&nbsp;Huifeng Li,&nbsp;Libo Yang,&nbsp;Zengjie Sun","doi":"10.1186/s42162-025-00533-5","DOIUrl":"10.1186/s42162-025-00533-5","url":null,"abstract":"<div><p>Load frequency control (LFC) in power systems faces increasingly complex cyber-physical attack threats, while existing detection methods have limited capability to identify intelligent attacks. This paper constructs an LFC system model considering dynamic response characteristics and establishes a reinforcement learning-based method for generating multiple attack strategies, covering typical scenarios such as false data injection (FDI) and load switching attacks. A multi-model fusion attack detection framework is proposed, integrating (Long Short-Term Memory) LSTM supervised learning and autoencoder unsupervised learning algorithms, with an adaptive weight adjustment mechanism that dynamically optimizes detection strategies. Experimental results demonstrate that the fusion mechanism achieves 99.4% comprehensive identification accuracy across four system states, outperforming single supervised models (98%) and single unsupervised models (76.4%). Detection accuracy exceeds 99% for three different frequency characteristic attacks, with an average detection delay of only 0.12 seconds. The fusion mechanism effectively reduces false positive and false negative rates (FNRs), showing significant advantages in identifying and defending against unknown attacks, providing a new approach for LFC system security protection.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00533-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135342","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":"Management system and optimal control for three-dimensional visualization and maintenance of thermal power plant","authors":"Zhiqiang Feng,&nbsp;Qiuxiang Liang,&nbsp;Mingyi Wei,&nbsp;Lei Li,&nbsp;Youzhu Bu,&nbsp;Yanqing Xin","doi":"10.1186/s42162-025-00491-y","DOIUrl":"10.1186/s42162-025-00491-y","url":null,"abstract":"&lt;div&gt;&lt;p&gt;With the evolution of energy pattern and the advancement of science and technology, the operation and maintenance management of thermal power plants has encountered bottlenecks. The traditional model is difficult to meet the current demand. The purpose of this study is to build an advanced three-dimensional (3D) visualization and maintenance system suitable for thermal power plants, and to optimize it with the technology of convolutional neural network (CNN). Firstly, literature research is carried out, and the achievements and existing shortcomings in related fields are deeply excavated. Then, this study systematically analyzes the operation and maintenance ecology of thermal power plants, focuses on equipment operation data trajectory and process flow context, and accurately anchors key pain points. Based on this, a basic 3D visualization and maintenance system is constructed. Its data acquisition and processing module is customized for thermal power generation conditions. It can accurately capture multiple data from core equipment such as boilers and steam turbines and integrate them efficiently. According to the actual situation and equipment details of the power plant, the 3D modeling module designs a highly realistic digital model. The visual interface module is user-experience-oriented, presenting an intuitive and convenient interactive window. It is convenient for operation and maintenance personnel to monitor and make efficient decisions in real time. Then, CNN technology is introduced to deeply analyze the data content and find out the operation and maintenance value. The experimental data shows the effectiveness, and the basic system performs well in the dimensions of accuracy, completeness and accuracy, with the numerical value exceeding 85%, which is more prominent than the traditional system. After optimization by CNN technology, the response time of the system is increased by 5%. The calculation cost is reduced by 15%, and the data throughput is increased by 13%. However, there is still room for improvement in the system. For example, the stability of data acquisition in complex electromagnetic and high-temperature environment needs to be strengthened. The calculation accuracy of the model for extreme working conditions and microscopic changes of equipment needs to be improved. The dimension of personalized customization of visual interface needs to meet the demands of multiple users. The system scalability needs to meet the requirements of technical iteration and equipment update, and the technical application process needs to be simplified for promotion. This study injects innovative vitality into the operation and maintenance management of thermal power plants, and significantly improves the quality and efficiency of operation and maintenance. Looking forward to the future, it is still necessary to test and analyze in many aspects and optimize in many dimensions to drive the operation and maintenance management of therma","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00491-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100437","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}