Energy Informatics最新文献

{"title":"The electromagnetic transient simulation acceleration algorithm based on delay mitigation of dynamic critical paths","authors":"Qi Guo,&nbsp;Yuanhong Lu,&nbsp;Jie Zhang,&nbsp;Jingyue Zhang,&nbsp;Libin Huang,&nbsp;Haiping Guo,&nbsp;Tianyu Guo,&nbsp;Liang Tu","doi":"10.1186/s42162-025-00516-6","DOIUrl":"10.1186/s42162-025-00516-6","url":null,"abstract":"<div><p>The task scheduling problem based on directed acyclic graphs (DAGs) has been proven to be NP-complete in general cases or under certain restrictions. In this paper, building upon existing scheduling algorithms, we introduce a static task scheduling algorithm based on directed acyclic graphs. By incorporating the proportion of task transmission delay as a guiding metric in the optimization process, processors can be prioritized for tasks with high latency, thereby improving computational efficiency. We first validate the theoretical feasibility of the algorithm using a theoretical case study and illustrate the algorithmic effectiveness using two real case studies, direct current (DC) model and alternating current (AC) model respectively. The research indicates that the scheduling algorithm proposed in this paper achieves an average scheduling length improvement of over 1.2% compared to the Heterogeneous Earliest-Finish-Time algorithm (HEFT) in topologies with high latency tasks. Additionally, the experiments show that the HEFT algorithm consumes 39.85us and the EMT-DM algorithm consumes 38.29us during simulation using DC, and the HEFT algorithm consumes 31.23us and the EMT-DM algorithm consumes 26.51us during simulation using AC, both of which are improved compared to the HEFT algorithm.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00516-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888645","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":"Machine learning-based inertia estimation in power systems: a review of methods and challenges","authors":"Santosh Diggikar,&nbsp;Arunkumar Patil,&nbsp;Siddhant Satyapal Katkar,&nbsp;Kunal Samad","doi":"10.1186/s42162-025-00496-7","DOIUrl":"10.1186/s42162-025-00496-7","url":null,"abstract":"<div><p>The transformation of power systems is accelerating due to the widespread integration of renewable energy sources (RES) and the growing role of inverter-based generations (IBGs). This shift has significantly reduced rotational inertia, increasing the system’s vulnerability to frequency fluctuations during disturbances. Consequently, the accurate and adaptive estimation of inertia has become crucial for maintaining frequency stability and grid reliability. Traditional estimation methods, though effective in certain scenarios, struggle to capture the non-linear and dynamic behaviors of modern power systems, necessitating the adoption of advanced solutions. This review comprehensively explores machine learning (ML)-based methodologies for inertia estimation, emphasizing their adaptability, scalability, and real-time capabilities compared to conventional approaches. The study categorizes ML techniques into supervised learning (SL), unsupervised learning (USL), semi-supervised learning (SSL), and reinforcement learning (RL), highlighting their applications, advantages, and limitations. Advanced methodologies, such as hybrid and ensemble models, are examined for their effectiveness in overcoming challenges posed by noisy data, dynamic behaviors, and complex grid configurations. Some advanced techniques demonstrate proficiency in analyzing complex datasets and providing real-time insights into the evolving dynamics of inertia. In addition to evaluating existing approaches, the review identifies key research gaps and emerging trends, offering strategic guidance and important considerations for the development of innovative ML-driven inertia estimation methods. By addressing these challenges, this study aims to support the creation of adaptive and reliable tools that ensure effective grid management in an energy ecosystem increasingly dominated by RES. </p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00496-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888648","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 building design based on green and low-carbon concept","authors":"Qian Lv","doi":"10.1186/s42162-025-00513-9","DOIUrl":"10.1186/s42162-025-00513-9","url":null,"abstract":"<div><p>The integration of modern technology and architectural design in intelligent buildings has led to advancements in functionality and user experience. These developments have also contributed to the pursuit of environmental sustainability, energy conservation, and emission reduction through the implementation of advanced technological systems. Guided by the concept of green and low-carbon, intelligent building design emphasizes the full utilization of renewable energy while utilizing advanced algorithms to optimize energy scheduling in intelligent buildings, achieving green, low-carbon, energy-saving, and emission-reduction goals. Therefore, based on the concept of green and low-carbon, this study optimizes the renewable energy system, lighting control system, elevator control system, and air conditioning control system of intelligent buildings. The experimental findings, utilizing a paradigmatic intelligent office building in Shanghai as a case study, demonstrated that the solar wind complementary power generation system of the building attained an annual power generation of 609,380 kWh. This amount satisfied 60% of the building's electricity requirement, thereby signifying a substantial breakthrough in conventional building energy supply methodologies. The lighting system adopted intelligent time lighting dual-mode control, reducing energy consumption by 10.1%. The optimization of the elevator group control algorithm could achieve an average monthly power saving of 6100 kWh. The air conditioning system reduced energy consumption by 7238 kWh/month through a load forecasting model. The results showed that the intelligent building energy optimization system established in the study, through multi-system algorithm linkage, improved overall energy efficiency by 23% compared to traditional solutions. This method provides a reusable technical paradigm for smart city emission reduction.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00513-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883552","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":"Fostering non-intrusive load monitoring for smart energy management in industrial applications: an active machine learning approach","authors":"Lukas Fabri,&nbsp;Daniel Leuthe,&nbsp;Lars-Manuel Schneider,&nbsp;Simon Wenninger","doi":"10.1186/s42162-025-00517-5","DOIUrl":"10.1186/s42162-025-00517-5","url":null,"abstract":"<div><p>Non-intrusive load monitoring (NILM) is a promising and cost-effective approach incorporating techniques that infer individual applications' energy consumption from aggregated consumption providing insights and transparency on energy consumption data. The largest potential of NILM lies in industrial applications facilitating key benefits like energy monitoring and anomaly detection without excessive submetering. However, besides the lack of feasible industrial time series data, the key challenge of NILM in industrial applications is the scarcity of labeled data, leading to costly and time-consuming workflows. To overcome this issue, we develop an active learning model using real-world data to intelligently select the most informative data for expert labeling. We compare three disaggregation algorithms with a benchmark model by efficiently selecting a subset of training data through three query strategies that identify the data requiring labeling. We show that the active learning model achieves satisfactory accuracy with minimal user input. Our results indicate that our model reduces the user input, i.e., the labeled data, by up to 99% while achieving between 62 and 80% of the prediction accuracy compared to the benchmark with 100% labeled training data. The active learning model is expected to serve as a foundation for expanding NILM adoption in industrial applications by addressing key market barriers, notably reducing implementation costs through minimized worker-intensive data labeling. In this vein, our work lays the foundation for further optimizations regarding the architecture of an active learning model or serves as the first benchmark for active learning in NILM for industrial applications.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00517-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879606","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":"Optimization of electric vehicle charging facility layout considering the enhancement of renewable energy consumption capacity and improvement of PSO algorithm","authors":"Di Zheng,&nbsp;Baobao Zheng","doi":"10.1186/s42162-025-00514-8","DOIUrl":"10.1186/s42162-025-00514-8","url":null,"abstract":"<div><p>By arranging the charging facilities of electric vehicles reasonably, electric vehicle users can be guided to charge during the peak period of renewable energy generation, improving their ability to consume this energy. To layout electric vehicle charging facilities, a single charging station optimization configuration model is constructed to provide optimal configuration parameter references for subsequent charging facility layout optimization models. In the optimization model, the study considers charging load calculation, site selection, and capacity determination. To deal with the optimization model, the particle swarm optimization is adopted and improved in three aspects. These three improvements include randomly updating inertia weights, introducing acceleration factors to replace learning factors, and introducing fast non-dominated sorting for better or worse selection, and improving the optimization ability of the algorithm by solving the crowding distance. The results showed that the maximum function values of the designed algorithm were 3.56 × 10^–14, 5.32 × 10⁰, and 1.08 × 10¹ for unimodal, multimodal, and composite functions, respectively, and the standard deviations of the algorithm were 2.01 × 10^–14, 3.557 × 10⁰, and 8.56 × 10^–1, all of which were smaller than comparison algorithms. In a single charging station, the expected values of photovoltaic power generation, energy storage system, and charging piles were 500 kW, 56.45 kW/20163 kW, and 680 kW, respectively. In terms of charging station location and charging facility capacity, there should be 7 charging locations and charging facilities. In summary, the designed model has good performance, and the optimized model can layout charging facilities. The research results can better promote the consumption of renewable energy, lower the construction cost, and optimize the utilization rate of charging facilities.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00514-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883699","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":"Data-driven power marketing strategy optimization and customer loyalty promotion","authors":"Bo Chen,&nbsp;Wei Cui","doi":"10.1186/s42162-025-00510-y","DOIUrl":"10.1186/s42162-025-00510-y","url":null,"abstract":"<div><p>In the context of intensifying competition within the power market, power companies face the dual challenges of enhancing customer loyalty and optimizing marketing strategies. This study addresses these challenges by employing the long-term and short-term memory (LSTM) network model to analyze data-driven power marketing strategies and their impact on customer loyalty. The LSTM model is trained on a dataset combining time-series power consumption data with customer interaction scores and market response rates. This enables the model to predict and explain customer responses to marketing efforts with greater accuracy. Unlike traditional marketing models, which lack the capacity to capture dynamic customer behavior over time, the LSTM model accounts for both the temporal nature of consumption patterns and static customer feedback, offering a more holistic view. Key findings indicate that improving the quality of customer service interaction and accurately targeting marketing activities significantly boosts customer loyalty. In particular, customer interaction scores and market response rates are the most influential factors driving customer loyalty, providing critical insights for companies to adjust their strategies effectively. This study’s novelty lies in its application of advanced machine learning methods, such as LSTM, to the power industry—a sector traditionally slower to adopt such innovations. By bridging this gap, the research provides actionable recommendations on how power companies can implement data-driven marketing strategies to improve service quality, increase customer retention, and enhance their competitive position in the market. Additionally, the results underscore the model’s effectiveness in forecasting and optimizing marketing outcomes, offering a scalable solution for the evolving power sector. In the power market, companies face challenges in enhancing customer loyalty and optimizing strategies. This study employs the LSTM network model, trained on combined time-series power consumption, customer interaction scores and market response rates data. Unlike traditional models that struggle with dynamic customer behavior capture, LSTM accounts for consumption pattern temporality and static feedback. It outperforms other techniques like Random Forest and XGBoost in handling time-dependent consumption data. The key findings highlight the importance of customer interaction and targeted marketing. By applying LSTM, power companies can better predict customer responses, optimize marketing, improve service quality and enhance competitive position, providing a scalable solution for the evolving power sector.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00510-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861350","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":"Transformer fault diagnosis using machine learning: a method combining SHAP feature selection and intelligent optimization of LGBM","authors":"Cheng Liu,&nbsp;Weiming Yang","doi":"10.1186/s42162-025-00519-3","DOIUrl":"10.1186/s42162-025-00519-3","url":null,"abstract":"<div><p>This paper proposes a novel approach for transformer fault diagnosis. Initially, a high-dimensional feature set comprising 19 features related to five gas concentrations is constructed to reflect the gas-fault relationship. Subsequently, the Shapley Additive Explanations (SHAP) method is employed to evaluate feature importance and select a subset that significantly influences model predictions, thereby simplifying the model and enhancing its interpretability. Following this, the bald eagle search (BES) intelligent optimization algorithm is utilized to optimize the hyperparameters of the light gradient boosting machine (LGBM) model, further improving its predictive capability. Comparative experiments with various traditional machine learning models validate the effectiveness of the proposed method. The SHAP-BES-LGBM model achieves the highest accuracy of 0.9509 and an f1 score of 0.9606 on the test set, with only 11 samples misclassified, demonstrating superior classification performance and underscoring the advantages of this integrated approach in transformer fault diagnosis.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00519-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861220","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":"Transforming the electrical grid: the role of AI in advancing smart, sustainable, and secure energy systems","authors":"T. A. Rajaperumal,&nbsp;C. Christopher Columbus","doi":"10.1186/s42162-024-00461-w","DOIUrl":"10.1186/s42162-024-00461-w","url":null,"abstract":"<div><p>The evolution of the electrical grid from its early centralized structure to today’s advanced “smart grid” reflects significant technological progress. Early grids, designed for simple power delivery from large plants to consumers, faced challenges in efficiency, reliability, and scalability. Over time, the grid has transformed into a decentralized network driven by innovative technologies, particularly artificial intelligence (AI). AI has become instrumental in enhancing efficiency, security, and resilience by enabling real-time data analysis, predictive maintenance, demand-response optimization, and automated fault detection, thereby improving overall operational efficiency. This paper examines the evolution of the electrical grid, tracing its transition from early limitations to the methodologies adopted in present smart grids for addressing those challenges. Current smart grids leverage AI to optimize energy management, predict faults, and seamlessly integrate electric vehicles (EVs), reducing transmission losses and improving performance. However, these advancements are not without limitations. Present grids remain vulnerable to cyberattacks, necessitating the adoption of more robust methodologies and advanced technologies for future grids. Looking forward, emerging technologies such as Digital Twin (DT) models, the Internet of Energy (IoE), and decentralized grid management are set to redefine grid architectures. These advanced technologies enable real-time simulations, adaptive control, and enhanced human–machine collaboration, supporting dynamic energy distribution and proactive risk management. Integrating AI with advanced energy storage, renewable resources, and adaptive access control mechanisms will ensure future grids are resilient, sustainable, and responsive to growing energy demands. This study emphasizes AI’s transformative role in addressing the challenges of the early grid, enhancing the capabilities of the present smart grid, and shaping a secure, efficient, and adaptive next-generation grid aligned with future needs.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00461-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143840284","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":"Optimization planning of new rural multi-energy distribution network based on fuzzy algorithm","authors":"Huanhuan Ye,&nbsp;Qing Wang,&nbsp;Yongsheng Xian,&nbsp;Bo Wen,&nbsp;Yuange Li,&nbsp;Siwei Hou","doi":"10.1186/s42162-025-00502-y","DOIUrl":"10.1186/s42162-025-00502-y","url":null,"abstract":"<div><p>With the increasing demand for renewable energy in new rural areas, the integration and optimization of multi-energy systems such as wind and photovoltaic have become a key issue in distribution network planning. Existing methods are difficult to cope with the volatility and uncertainty of energy sources, resulting in uneven load distribution, high energy loss and low system efficiency. In this paper, the fuzzy algorithm is used to optimize the multi-energy distribution network, and the efficiency of the system is improved by real-time scheduling and load balancing. The results show that the fuzzy algorithm can effectively improve the utilization rate of renewable energy, reduce energy loss, and improve the stability and load matching degree of the system, which provides an optimization scheme for the new rural multi-energy system.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00502-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826586","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 novel framework for optimizing residential load response planning with consideration of user satisfaction","authors":"Mohammad Hossein Erfani Majd,&nbsp;Gholam-Reza Kamyab,&nbsp;Saeed Balochian","doi":"10.1186/s42162-025-00504-w","DOIUrl":"10.1186/s42162-025-00504-w","url":null,"abstract":"<div><p>This study presents an optimization framework for residential energy management that integrates photovoltaic (PV) systems, battery storage, and demand response strategies. The primary objective is to minimize electricity costs while ensuring efficient use of renewable energy resources. The proposed method utilizes the Meerkat Optimization Algorithm (MOA), which is compared against other optimization algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Teaching-Learning-Based Optimization (TLBO). The results show that the proposed MOA achieves significant cost reductions. For example, under Time-of-Use (TOU) tariffs, the total electricity cost is reduced by 14% compared to the base case, while under Real-Time Pricing (RTP), the reduction is 16%. The optimized system also yields a 5 kW PV system and a 10 kWh battery, compared to 3 kW PV and 6 kWh battery in the GA and PSO cases. Additionally, the MOA provides a more computationally efficient solution, with a calculation time of 73 s, compared to 91 s for GA and 102 s for PSO. This study demonstrates the effectiveness of the MOA in optimizing residential energy systems, providing a robust solution for reducing electricity costs while integrating renewable energy sources. The approach is generalizable to other energy management applications and can be adapted for various regions and household configurations.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00504-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809136","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}