Energy Informatics最新文献

{"title":"Approach for energy efficient building design during early phase of design process","authors":"Aviruch Bhatia,&nbsp;Shanmukh Dontu,&nbsp;Vishal Garg,&nbsp;Reshma Singh","doi":"10.1186/s42162-024-00426-z","DOIUrl":"10.1186/s42162-024-00426-z","url":null,"abstract":"<div><p>Energy consumption in the building sector is about 40% of total energy consumed globally and is trending upwards, along with its contribution to greenhouse gas (GHG) emissions. Given the adverse impacts of GHG emissions, it is crucial to integrate energy efficiency into building designs. The most significant opportunities for enhancing energy performance are present during the initial phases of building design, when there is less impact of other design constraints. Various tools exist for simulating different design options and providing feedback in terms of energy consumption and comfort parameters. These simulation outputs must then be analyzed to derive design solutions. This paper presents an innovative approach that utilizes user input parameters, processes them through cloud computing, and outputs easily understandable strategies for energy-efficient building design. The methodology employs Asynchronous Distributed Task Queues (DTQ) - a more scalable and reliable alternative to conventional speedup techniques-for conducting parametric energy simulations in the cloud. The goal of this approach is to assist design teams in identifying, visualizing, and prioritizing energy-saving design strategies from a range of possible solutions for each project. Furthermore, a tool ‘eDOT’ has been developed utilizing the discussed methodology. Unlike existing tools, eDOT leverages artificial intelligence to dynamically generate and provide design strategies during the early phases of design process. By simplifying the simulation process, eDOT enables design teams to make informed, data-driven decisions without needing to interpret complex simulation outputs. A case study simulated for two locations is provided in this paper to demonstrate the effectiveness of eDOT, further underscoring its practical impact on energy-efficient building design.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00426-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of multi-sensor information fusion technology in fault early warning of smart grid equipment","authors":"Zhihui Kang,&nbsp;Yanjie Zhang,&nbsp;Yuhong Du","doi":"10.1186/s42162-024-00433-0","DOIUrl":"10.1186/s42162-024-00433-0","url":null,"abstract":"<div><p>The purpose of this paper is to improve the fault early warning effect of smart grid equipment through multi-sensor information fusion technology. Therefore, based on the analytical model of power grid fault diagnosis, this paper considers the influence of distributed generation in distribution network on fault diagnosis, as well as the misoperation or refusal of protection and switch, and the false alarm or leakage of alarm signal. At the same time, in order to display the results of fault diagnosis accurately and intuitively, an analytical model of fault diagnosis of distribution network based on multi-source information fusion is proposed. Finally, this paper verifies the effectiveness of this method through an example application. This article uses the PEDL dataset for experimental research, Through the comparison of fault data, it can be seen that compared with existing methods, the method proposed in this paper achieves the highest goodness of fit for warning, indicating the best fault warning effect.When there is enough training set, the prediction accuracy of the fault set can reach over 99%, Based on experimental analysis, it can be concluded that the proposed power grid equipment model has higher accuracy and reliability compared to traditional models. And the model in this article integrates the real-time monitoring function of power grid equipment and the equipment fault warning function, which improves the practicality of the power grid equipment monitoring system.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00433-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672695","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 energy acquisition system in smart grid based on artificial intelligence and digital twin technology","authors":"Zhen Jing,&nbsp;Qing Wang,&nbsp;Zhiru Chen,&nbsp;Tong Cao,&nbsp;Kun Zhang","doi":"10.1186/s42162-024-00425-0","DOIUrl":"10.1186/s42162-024-00425-0","url":null,"abstract":"<div><p>In response to the low operating speed and poor stability of energy harvesting systems in smart grids, an energy harvesting optimization method based on improved convolutional neural networks and digital twin technology is proposed in the experiment. Firstly, a smart grid data transmission framework integrating digital twin technology is proposed. A digital twin mapping method based on time, data, and topology structure is used to realize the digital twin mapping at the device level of power grid. Through data synchronization and interaction between the physical power grid and the digital twin model, the operational efficiency and reliability of the power grid are improved. Then, the classical convolutional neural network and attention mechanism are used to comprehensively analyze the physical topology data in the smart grid energy acquisition system. The improved lightweight target detection model is combined to monitor the equipment status of the smart grid and extract key features. Simultaneously utilizing convolutional attention mechanism to dynamically adjust the feature weights of channels or spaces, completing the preprocessing of energy harvesting data. Finally, combined with energy harvesting and power grid switching system, the process of energy harvesting and power grid operation are optimized together. On the training and validation sets, when the channels exceeded 60, the proposed method achieved a system energy efficiency of 55% during operation. The system energy efficiency of the other three comparative algorithms was all less than 40%. In practical applications, as the energy transfer loss increased to 1.0, the system throughput increased to 50 bits. The electricity needs of different users were met, and the difference between power allocation and optimal power allocation was small, which was very reasonable. This proves that the research has effectively optimized the energy harvesting system in the smart grid, improving the efficiency and reliability of the system in practical applications of the smart grid. At the same time, in the increasingly severe energy problem, this system can further provide technical references for the utilization of renewable energy and help achieve the goal of sustainable energy.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00425-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672702","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":"Water resource vulnerability assessment in Hubei Province: a case study","authors":"Qiong Li,&nbsp;Jian Zhou,&nbsp;Zhinan Zhang","doi":"10.1186/s42162-024-00419-y","DOIUrl":"10.1186/s42162-024-00419-y","url":null,"abstract":"<div><p>In view of the different views of academia on the weight allocation of vulnerability assessment indicators, this study creatively proposed a data-based objective evaluation framework of water resource vulnerability, and applied it to the evaluation of water resource vulnerability in Hubei Province. According to the conceptual model of DPSIR proposed by the United Nations, five vulnerability factors are proposed: driving force, pressure, state, influence and response. In this study, 15 indicators were selected and the projection tracing model was used to identify vulnerability. Aiming at the complex problem of optimization calculation of projection index function in the projection tracing model, the accelerated genetic algorithm is used to speed up the optimization speed, solves the optimization problem in the process of projection tracing, and determines the objective weight of all indicators. Example calculation shows that the model can deal with complex multi-index optimization problems, and is an effective way to solve the comprehensive evaluation of complex vulnerability, and the weighting method is important for the evaluation of water resources vulnerability. The results of this paper show that the combination of projection tracing method and machine learning algorithm can improve the efficiency, objectivity and accuracy of high-dimensional data analysis, and can provide scientific basis for policy makers.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00419-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672676","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 scheduling of microgrid cluster based on improved moth-flame algorithm","authors":"Yaping Li,&nbsp;Zhijun Zhang,&nbsp;Zhonglin Ding","doi":"10.1186/s42162-024-00418-z","DOIUrl":"10.1186/s42162-024-00418-z","url":null,"abstract":"<div><p>With the rapid development of renewable energy, microgrid cluster systems are gradually being applied. To promote the development of microgrid cluster scheduling technology, maximize economic benefits while reducing the operating cost required for microgrid scheduling, an optimized scheduling scheme is proposed by constructing a function to minimize the operating cost of microgrids. Then, chaos mutation and Gaussian mutation are applied to improve the moth-flame algorithm that easily falling into local optima. A microgrid cluster optimization scheduling model on the basis of the improved moth-flame algorithm is constructed. The experimental results showed that the operating cost in islanding mode was 4286.21 yuan after 160 iterations. After optimizing the scheduling, the operating cost was 3912.3 yuan, with a decrease of 8.7%. The improved moth-flame algorithm had a stable average loss value of 20% and an operating efficiency of 97.19% after 10–50 iterations, which was significantly higher than other intelligent algorithms. This indicates that the improved moth-flame algorithm has high reliability and effectiveness in microgrid cluster optimization scheduling. Therefore, the proposed model effectively optimizes the scheduling scheme of microgrid cluster, providing new solutions for the efficient utilization of smart grids and renewable energy in the future.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00418-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645385","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":"Quantitative assessment and optimization strategy of flexibility supply and demand based on renewable energy high-penetration power system","authors":"Liangliang Zhang,&nbsp;Yimin Chu,&nbsp;Yanhua Xu,&nbsp;Wei Guo","doi":"10.1186/s42162-024-00431-2","DOIUrl":"10.1186/s42162-024-00431-2","url":null,"abstract":"<div><p>With the transformation of the global energy structure, the high penetration rate of renewable energy in power systems has become a trend. This article focuses on the quantitative evaluation and optimization strategies for the flexible supply and demand of renewable energy high-p penetration power systems. Using a combination of data-driven and model simulation methods, the flexibility requirements of the power system after integrating renewable energy are accurately quantified. The impact of uncertainty in renewable energy output on system flexibility was evaluated through system flexibility analysis and scenario construction techniques, and effective flexibility improvement strategies were proposed in combination with optimized scheduling design. The research results show that under high penetration of renewable energy, there is an imbalance between the supply and demand of flexibility in the power system. When the proportion of renewable energy installed capacity reaches 40%, the system flexibility gap reaches 10%. A comprehensive optimization strategy has been proposed to address this issue, including constructing energy storage facilities, demand side response, and virtual power plants. After implementing these measures, the flexibility gap of the system can be reduced to less than 5%, which can effectively ensure the stable operation of the power system.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-024-00431-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636821","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":"Distribution grid monitoring based on feature propagation using smart plugs","authors":"Simon Grafenhorst,&nbsp;Kevin Förderer,&nbsp;Veit Hagenmeyer","doi":"10.1186/s42162-024-00427-y","DOIUrl":"10.1186/s42162-024-00427-y","url":null,"abstract":"<div><p>Smart home power hardware makes it possible to collect a large number of measurements from the distribution grid with low latency. However, the measurements are imprecise, and not every node is instrumented. Therefore, the measured data must be corrected and augmented with pseudo-measurements to obtain an accurate and complete picture of the distribution grid. Hence, we present and evaluate a novel method for distribution grid monitoring. This method uses smart plugs as measuring devices and a feature propagation algorithm to generate pseudo-measurements for each grid node. The feature propagation algorithm exploits the homophily of buses in the distribution grid and diffuses known voltage values throughout the grid. This novel approach to deriving pseudo-measurement values is evaluated using a simulation of SimBench benchmark grids and the IEEE 37 bus system. In comparison to the established GINN algorithm, the presented approach generates more accurate voltage pseudo-measurements with less computational effort. This enables frequent updates of the distribution grid monitoring with low latency whenever a measurement occurs.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00427-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600787","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":"New energy vehicle battery state of charge prediction based on XGBoost algorithm and RF fusion","authors":"Changyou Lei","doi":"10.1186/s42162-024-00424-1","DOIUrl":"10.1186/s42162-024-00424-1","url":null,"abstract":"<div><p>As the most important component of new energy electric vehicles, lithium-ion batteries may suffer irreversible damage to the battery due to an abnormal state of charge. Nevertheless, the extant research on charge prediction predominantly employs a single model or an enhanced single model. However, these approaches do not fully account for the intricacies and variability of the actual driving road conditions of the vehicle. Furthermore, the prediction accuracy of the charge state in the latter phase of discharge remains suboptimal. To further improve the accuracy of predicting the state of charge, the study utilizes actual operating data of new energy vehicles and combines two proposed algorithms to build a first layer learner of a fusion prediction model. The second layer learner integrates various prediction results. The fusion model can enhance its adaptability to complex data structures by combining the gradient boosting ability of XGBoost algorithm and the diversity of Random Forest when dealing with nonlinear problems. This fusion method modifies the input features of the second layer of the fusion model, enhances the complexity of the second layer learner, effectively circumvents overfitting, and exhibits reduced error rates relative to traditional single-chip prediction models. As a result, the performance of the prediction model is significantly enhanced. The tests showed that when using the fusion model for state of charge prediction, the prediction accuracy could reach 97.6%, and the prediction accuracy was higher than the other four comparison models. When the car was driving in a 25 ℃ highway environment, the root mean square error of the fusion model was 1.3%, and the average absolute error was 1.5%. In urban road environments, the root mean square error of the fusion model was 1.5%, and the average absolute error was 1%. The experiment demonstrates that the proposed fusion prediction model can accurately predict the charging status, thereby enabling the battery to be fully utilized while simultaneously reducing energy consumption. In comparison to the traditional single model or enhanced single model, the proposed fusion model has demonstrated a notable enhancement in both prediction accuracy and computational efficiency.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00424-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598873","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":"Analysis of user behavior and energy-saving potential of electric water heaters","authors":"Jialin Liu,&nbsp;Xue Bai,&nbsp;Yujuan Xia,&nbsp;Yan Bai,&nbsp;Yanrong Kong","doi":"10.1186/s42162-024-00423-2","DOIUrl":"10.1186/s42162-024-00423-2","url":null,"abstract":"<div><p>As global energy resources get more limited and environmental problems worsen, it is crucial to enhance energy efficiency and reduce energy consumption in end-use products. This research focuses on electric water heaters, a significant household energy consumer, and collects a large amount of data through questionnaires and analyzes the current usage patterns of water heater use, as well as the impact of the users’ personal characteristics and energy-saving consciousness on usage behaviors. It also evaluates the energy-saving potential under different scenarios, considering both consumer behaviors and product efficiency levels. Results indicate that a substantial number of users still purchase high-energy-consuming water heaters and fail to adjust temperatures according to their specific needs, resulting in considerable energy waste. Electric water heaters exhibit significant potential for energy savings, with the efficiency of the product and user behaviors identified as key factors influencing overall energy consumption. The study provides important insights into the usage behavior of electric water heaters and offers actionable recommendations for manufacturers and government agencies: advocating the use of certified energy-efficient water heaters, raising public awareness of energy efficiency in appliance use, etc., which is in line with the country’s goals of energy conservation and environmental sustainability.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00423-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595615","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 a digital twin model for incremental aggregation of multi type load information in hybrid microgrids under integrity constraints","authors":"Yibo Lai,&nbsp;Libo Fan,&nbsp;Weiyan Zheng,&nbsp;Rongjie Han,&nbsp;Kai Liu","doi":"10.1186/s42162-024-00404-5","DOIUrl":"10.1186/s42162-024-00404-5","url":null,"abstract":"<div><p>In the multi type load information of hybrid microgrids, data loss or incompleteness may occur due to network congestion, signal interference, equipment failures, and other reasons. Especially with the continuous generation of new load data, gradually incorporating these new data into the existing aggregation process to achieve continuous updating and optimization of load information. Therefore, this article proposes a digital twin model construction method for incremental aggregation of multi type load information in hybrid microgrids under integrity constraints. The Leida criterion and cubic exponential smoothing method are used to preprocess various load data of hybrid microgrids, remove abnormal data, reduce data fluctuations, and make the data more interpretable. Establish integrity constraints for multiple load data of hybrid microgrids and extract load characteristics of hybrid microgrids. Based on these, establish a digital twin model for the incremental aggregation of multiple load information in a hybrid microgrid, and solve the model using an improved K-means algorithm to achieve continuous updating and optimization of load information. The experimental results show that the data sharing delay of this method is 0.12 s, the load is basically consistent with the actual value, and the relative error of the load data is 4%.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-024-00404-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587798","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}