Energy Informatics最新文献

{"title":"Distributed photovoltaic cluster output monitoring method based on time series data acquisition","authors":"Hua Ye,&nbsp;Xuegang Lu,&nbsp;Wei Zhang,&nbsp;Fei Cheng,&nbsp;Ying Zhao","doi":"10.1186/s42162-025-00480-1","DOIUrl":"10.1186/s42162-025-00480-1","url":null,"abstract":"<div><p>The data processing efficiency of distributed photovoltaic cluster output monitoring needs to be improved, improving the prediction effect of distributed photovoltaic power station cluster can effectively improve the security of power system operation and reduce the difficulty of power grid management. In order to obtain a reliable distributed photovoltaic cluster output monitoring method, this paper analyzes the output relationship of cluster power stations, combining time series data analysis methods for distributed cluster processing and monitoring data processing, a combined model of ceemdan and Bayesian neural network is proposed, the representative power plant prediction values obtained by establishing a combination model are weighted to obtain the cluster output prediction values. Compared with the simple superposition of the predicted values of cluster power stations, the average absolute error of this method is reduced by 3.3%, and the root mean square error is reduced by 5.86%. It is concluded that this model can effectively predict the power stations in the cluster. According to the experimental analysis, the output monitoring method of distributed photovoltaic clusters based on time series data collection proposed in this paper has certain effects and can provide theoretical support for the further development of distributed photovoltaic clusters.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00480-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446429","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":"Impact of digital transformation on corporate sustainability: evidence from China’s carbon emissions","authors":"Jiaomei Tang,&nbsp;Kuiyou Huang,&nbsp;Ailing Xiong","doi":"10.1186/s42162-025-00479-8","DOIUrl":"10.1186/s42162-025-00479-8","url":null,"abstract":"<div><p>Climate change has become an increasingly pressing issue, underscoring the urgent global need for energy conservation and emission reduction. As one of the largest emitters, China is actively advancing comprehensive efforts to reduce emissions in pursuit of sustainable development, with enterprises playing a key role in aligning economic growth with environmental sustainability. Digital Transformation (DT) has emerged as a crucial enabler of low-carbon economic development. This study utilizes data from publicly listed companies in China, spanning the period from 2000 to 2021, and employs a two-way fixed-effects model to assess the impact of corporate DT on Carbon Emissions (CE). The findings reveal that: First, DT significantly contributes to the reduction of CE; Second, the impact of DT on CE varies across regions, industries, and firm characteristics; Third, the positive effect of DT on CE is driven by mechanisms such as technological advancement, innovation promotion, resource optimization, and improved output efficiency. These results provide both theoretical insights and empirical evidence supporting the role of DT in fostering green, low-carbon enterprise development.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00479-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423221","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":"Hybrid feature-based neural network regression method for load profiles forecasting","authors":"Aidos Satan,&nbsp;Nurkhat Zhakiyev,&nbsp;Aliya Nugumanova,&nbsp;Daniel Friedrich","doi":"10.1186/s42162-025-00481-0","DOIUrl":"10.1186/s42162-025-00481-0","url":null,"abstract":"<div><p>This study addresses the critical need for improved demand forecasting models that can accurately predict energy consumption, particularly in the context of varying geographical and climatic conditions. The work introduces a novel demand forecasting model that integrates clustering techniques and feature engineering into neural network regression, with a specific focus on incorporating correlations with air temperature. Evaluation of the model’s efficacy utilized a benchmark dataset from Tetouan, Morocco, where existing forecasting methods yielded RMSE values ranging from 6429 to 10,220 [MWh]. In contrast, the proposed approach achieved a significantly lower RMSE of 5168, indicating its superiority. Subsequent application of the model to forecast demand in Astana, Kazakhstan, as a case study, showcased its efficacy further. Comparative analysis against a baseline neural network method revealed a notable improvement, with the proposed model exhibiting a MAPE of 5.19% compared to the baseline’s 17.36%. These findings highlight the potential of the proposed approach to enhance demand forecasting accuracy, particularly across diverse geographical contexts, by leveraging climate-related inputs, the methodology also demonstrates potential for broader applications, such as flood forecasting, agricultural yield prediction, or water resource management.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00481-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379845","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":"Sustainable PV-hydrogen-storage microgrid energy management using a hierarchical economic model predictive control framework","authors":"Xinyu Guo,&nbsp;Faying Gu,&nbsp;Hongxu Liu,&nbsp;Yongcheng Yu,&nbsp;Runjie Li,&nbsp;Juan Wang","doi":"10.1186/s42162-025-00482-z","DOIUrl":"10.1186/s42162-025-00482-z","url":null,"abstract":"<div><p>Hydrogen-based renewable microgrid is considered as a prospective technique in power generation to reduce the carbon footprint, combat climate change and promote renewable energy sources integration. The photovoltaic-hydrogen-storage (PHS) microgrid system cleverly integrates renewable clean energy and hydrogen storage, providing a sustainable solution that maximizes the solar energy utilization. However, the changeable weather conditions and fluid market make it challenging to manage energy balance of the system. Moreover, in view of the fact that the existing energy management systems often ignore the dynamic synergy of microgrids, a hierarchical economic model predictive control (HEMPC) framework is proposed to realize the optimal operation of PHS microgrid. First, a precise nonlinear model of the PHS microgrid is established and the logic variables are introduced to capture the hydrogen devices’ short-term properties, i.e., the start-up/shut-down of electrolyzers and fuel cells. Then a comprehensive economic cost function, including internal power demand tracking cost, system operation cost and contract deviation cost, is considered in the proposed two-level HEMPC framework in order to address challenges such as fluctuating weather conditions, dynamic market environments, and the often-overlooked dynamic synergy of microgrid components. Under the proposed framework, a mixed-integer nonlinear optimization problem is solved by the long-term EMPC in the upper level to regulate the start-up/shut-down of hydrogen devices and the state of charge in the battery, and the short-term EMPC in the lower level reoptimizes the power demand tracking cost while tracking the optimal reference signal from the long-term EMPC, thereby improving overall control system efficiency. The simulation results along with qualitative and quantitative analysis show that compared with rule-based control, the proposed HEMPC is effective in managing the equipment power output, realizing dynamic synergy and enhancing the economic performance.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00482-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362036","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 and optimization of distributed energy management system based on edge computing and machine learning","authors":"Nan Feng,&nbsp;Conglin Ran","doi":"10.1186/s42162-025-00471-2","DOIUrl":"10.1186/s42162-025-00471-2","url":null,"abstract":"<div><p>With the continuous growth of global energy demand and the rapid development of renewable energy, traditional energy management systems are facing enormous challenges, especially in the scheduling and optimization of distributed energy. In order to meet these challenges, edge computing and machine learning technology are widely used in the design and optimization of distributed energy management systems. This paper proposes a design scheme of distributed energy management system based on edge computing and machine learning, and optimizes it. The system reduces data transmission latency and improves energy scheduling efficiency by performing real-time data processing and analysis on edge devices. The experimental results show that the proposed system performs outstandingly in optimizing energy allocation, reducing energy consumption, and improving system response speed. Specifically, by using machine learning algorithms for dynamic scheduling of distributed energy resources, the system can achieve an energy utilization rate 12% higher than traditional scheduling methods, and reduce energy waste by 18% in the event of fluctuations in energy demand. In addition, the system response time has been improved by 30% compared to traditional cloud-based solutions. These optimizations not only reduce energy costs, but also effectively enhance the sustainability and intelligence level of distributed energy systems. The contribution of this research lies in the combination of edge computing and machine learning technology to achieve real-time optimal control of the distributed energy system, reduce the system’s computing load and delay, and improve the accuracy and flexibility of energy management through data-driven methods. Future research can further explore how to integrate multiple machine learning algorithms to optimize energy scheduling strategies and improve the system’s adaptability in complex environments.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00471-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143108158","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":"EVLearn: extending the cityLearn framework with electric vehicle simulation","authors":"Tiago Fonseca,&nbsp;Luis Lino Ferreira,&nbsp;Bernardo Cabral,&nbsp;Ricardo Severino,&nbsp;Kingsley Nweye,&nbsp;Dipanjan Ghose,&nbsp;Zoltan Nagy","doi":"10.1186/s42162-024-00445-w","DOIUrl":"10.1186/s42162-024-00445-w","url":null,"abstract":"<div><p>Intelligent energy management strategies, such as Vehicle-to-Grid (V2G) and Grid-to-Vehicle (V1G) emerge as a potential solution to the Electric Vehicles’ (EVs) integration into the energy grid. These strategies promise enhanced grid resilience and economic benefits for both vehicle owners and grid operators. Despite the announced perspective, the adoption of these strategies is still hindered by an array of operational problems. Key among these is the lack of a simulation platform that allows to validate and refine V2G and V1G strategies. Including the development, training, and testing in the context of Energy Communities (ECs) incorporating multiple flexible energy assets. Addressing this gap, first we introduce the EVLearn, an open-source extension for the existing CityLearn simulation framework. EVLearn provides both V2G and V1G energy management simulation capabilities into the study of broader energy management strategies of CityLearn by modeling EVs, their charging infrastructure and associated energy flexibility dynamics. Results validated the extension of CityLearn, where the impact of these strategies is highlighted through a comparative simulation scenario.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00445-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143108154","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":"Vulnerability analysis on random matrix theory for power grid with flexible impact loads","authors":"Chuan Long,&nbsp;Shengyong Ye,&nbsp;Xinying Zhu,&nbsp;Minghai Xu,&nbsp;Xinting Yang,&nbsp;Yuqi Han,&nbsp;Liyang Liu","doi":"10.1186/s42162-024-00458-5","DOIUrl":"10.1186/s42162-024-00458-5","url":null,"abstract":"<div><p>The stochastic volatility of the rail transit load brings greater uncertainty to the vulnerability of the power grid. To solve the problem of the inaccurate results caused by the incomplete time-domain simulation model of the power system with rail transit load integration, this paper proposes a vulnerability analysis method for the power system with rail transit load integration based on the random matrix theory. In this paper, we first constructed a rail transit load model based on Deep Convolutional Generative Adversarial Networks (DCGAN) to simulate the situation that massive rail transit load merged into the Grid Scenario. Then, we generate a high-dimensional random matrix based on the power flow of the grid-connected system under different rail transit loads. Then, we construct a vulnerability analysis model combining the random matrix theory and the real-time separation window. Finally, we take the IEEE-39 bus system and a regional power grid in China as examples to evaluate the vulnerability of the grid-connected system. The results show that our method quantifies not only the impact of the rail transit load volatility on the system vulnerability, but the system endurance under different capacities of the rail transit load connected to grid. Moreover, it also provides a new way for system planning and safety monitoring in the power system with rail transit load integration.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00458-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109948","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":"Impact and optimization of vehicle charging scheduling on regional clean energy power supply network management","authors":"Penghui Xu,&nbsp;Xiaobo Wang,&nbsp;Zhichao Li","doi":"10.1186/s42162-025-00476-x","DOIUrl":"10.1186/s42162-025-00476-x","url":null,"abstract":"<div><p>Driven by the global energy transition, the widespread use of electric vehicles has profoundly reshaped the transportation landscape and thrown many problems to the power system, and coordinating their charging needs with renewable energy generation has become a key part of ensuring the stable operation of regional clean energy power supply networks. This study focuses on the problem of vehicle charging dispatch to make a breakthrough, deeply analyzes the effect and efficiency of the clean energy grid, and then proposes a series of targeted measures to effectively improve the operational efficiency and reliability of the energy system. The comprehensive model integrates electric vehicle charging stations, distributed photovoltaic power generation systems, wind farms, and battery energy storage devices and enables the charging process to be accurately controlled with real-time monitoring and intelligent algorithms. In particular, the demand forecasting model based on machine learning effectively solves the dilemma of matching the charging load with a clean energy supply. Experimental data strongly confirms that the optimization strategy has led to a 15% reduction in peak load on the grid, a 23% increase in the proportion of clean energy consumption, and a 10% reduction in total electricity consumption. For policymakers, these achievements can be used as a guide to help formulate energy policies and build a framework for adapting to the development of new energy. For practitioners, they serve as a guide to energy planning, grid dispatch, and technology research and development to improve effectiveness. The research promotes the growth of green energy, optimizes the energy structure, lays the foundation for a low-carbon and environmentally friendly society, affects the economy, environment, culture, and other fields, and becomes a key force driving sustainable development.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00476-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109795","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 new energy power plant network traffic anomaly detection method based on EMD","authors":"Danni Liu,&nbsp;Shengda Wang,&nbsp; YutongLi,&nbsp;Ji Du,&nbsp;Jia Li","doi":"10.1186/s42162-025-00474-z","DOIUrl":"10.1186/s42162-025-00474-z","url":null,"abstract":"<div><h3>Overview</h3><p>As Photovoltaic (PV) systems connect into the grid and depend on digital technology, risks develop from obsolete components, insufficient security measures, and insecure access points.</p><h3>Objectives</h3><p>Improve the safety and dependability of the main data communication network by conducting research on Proactive Coordinated Fault-Tolerant Federation (PCFT) security structure, building an early warning simulation to handle power data interactions network abnormalities, studying algorithms and technologies for abnormal traffic control, and effectively managing abnormal network traffic like DDoS, network scanning, along with surge traffic; Improve the capability of the SLA hierarchical service, enhance the service quality of the core services executed through the backbone network, as well as strengthen the security capability of the access system for the new energy power plant’s communication network by combing and analyzing the traffic of the main network of the current information communication network.</p><h3>Methodology</h3><p>This research propose Network Quality Assessment (NQA) traffic management algorithms to prevent illegal access and data breaches, this involves strong security measures such as encryption, firewalls, and encrypted communication methods. To maximize the efficiency of solar energy systems and allow for prompt maintenance, our suggested framework provides a practical and dependable method for detecting anomalies in PV cells in real-time. The incorporation of these state-of-the-art convolutional methods into the CNN-GRU model enhances detection capabilities and opens up new avenues for exploration in the realm of anomaly detection based on deep learning.</p><h3>Results</h3><p>The grid deployment of large-scale PV power facilities relies heavily on dependable communication networks. The efficiency of PV power plants and their ability to meet application requirements are both impacted by the communication infrastructures that are responsible for real-time monitoring. Presenting simulations of the communication networks of the PV power system, this research sought to evaluate possible futures of PV power plants. We test our model on a massive PV cell dataset and show that it outperforms state-of-the-art approaches in terms of resilience, speed, and accuracy of identification.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00474-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109454","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 power plant security issues monitoring and fault detection using attention based LSTM model","authors":"Shengda Wang,&nbsp;Zeng Dou,&nbsp;Danni Liu,&nbsp;Han Xu,&nbsp;Ji Du","doi":"10.1186/s42162-025-00473-0","DOIUrl":"10.1186/s42162-025-00473-0","url":null,"abstract":"<div><h3>Overview</h3><p>For Photo Voltaic (PV) arrays and Wind systems to operate as efficiently and effectively as possible, fault detection is essential. It is possible to improve the safety of renewable energy systems and guarantee that the service will continue uninterrupted if problem detection and diagnostics are performed in a timely and accurate manner. In general, wind power is one of the three major renewable energy sources, along with solar power and hydropower. Wind power is well distributed around the world, making it suitable to be exploited in human activities for the general welfare of society.</p><h3>Objectives</h3><p>A prototype security situational awareness system applicable to the power data communication network service and traffic model should be developed. This will help to successfully enhance the security and service quality of the power data communication network, effectively cope with network security threats in the new environment, and ensure the security of the power plant network access. The traffic of the main network of the existing data communication network will be combined and analyzed, and NQA traffic management algorithms will be studied and proposed. These actions will improve the SLA hierarchical service capability, the service quality of the core services carried by the backbone network, and strengthen the security capability of the new energy power plant communication network access system.</p><h3>Methodology</h3><p>For the purpose of this investigation, an attention-based long short-term memory (Att-LSTM) model was used for the categorization of time series actual data. The approach that has been developed is able to identify defects in photovoltaic arrays and inverters, which offers a dependable option for improving the efficiency and dependability of solar energy systems. For the purpose of evaluating the proposed method, a real-world solar energy dataset is used.</p><h3>Results</h3><p>The findings acquired from this evaluation are compared to the results received from existing detection approaches such as Cryptography, Intrusion Detection System (IDS) methods, and Network Defense Schemes. The results obtained demonstrate that the suggested method surpasses current fault detection techniques, providing greater accuracy and better performance.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00473-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109338","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}