{"title":"The real-time state identification of the electricity-heat system based on Borderline-SMOTE and XGBoost","authors":"Xin Pei, Fei Mei, Jiaqi Gu","doi":"10.1049/cps2.12032","DOIUrl":null,"url":null,"abstract":"<p>It is meaningful to study the real-time state monitoring and identification of integrated energy system and grasp its state in time for stable operation. A state identification method based on multi-class data equalisation and extreme gradient boost (XGBoost) is proposed for integrated energy systems. First, Latin hypercube sampling is used to simulate the load at different moments. Different system states are set up and combined with the simulative load at different moments to determine the system operation state at different moments. Then, the energy flow model is used to calculate the system power flow under different states, and the feature indexes are obtained to form the original data set. Aiming at the unbalanced data, the oversampling technology is used to preprocess data to achieve the balance of data sets. The pre-processed data is utilised to train the XGBoost, and the optimal hyperparameters of the model are obtained based on the K-fold cross-validation and grid search. Finally, the pre-processed data set is used to verify the proposed method. The calculation results show the accuracy of the identification model reaches 87.79%. Compared with traditional methods, the model can accurately identify the operating state of the electricity–heat energy system at any time section.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"8 4","pages":"236-246"},"PeriodicalIF":1.7000,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12032","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Cyber-Physical Systems: Theory and Applications","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cps2.12032","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 1
Abstract
It is meaningful to study the real-time state monitoring and identification of integrated energy system and grasp its state in time for stable operation. A state identification method based on multi-class data equalisation and extreme gradient boost (XGBoost) is proposed for integrated energy systems. First, Latin hypercube sampling is used to simulate the load at different moments. Different system states are set up and combined with the simulative load at different moments to determine the system operation state at different moments. Then, the energy flow model is used to calculate the system power flow under different states, and the feature indexes are obtained to form the original data set. Aiming at the unbalanced data, the oversampling technology is used to preprocess data to achieve the balance of data sets. The pre-processed data is utilised to train the XGBoost, and the optimal hyperparameters of the model are obtained based on the K-fold cross-validation and grid search. Finally, the pre-processed data set is used to verify the proposed method. The calculation results show the accuracy of the identification model reaches 87.79%. Compared with traditional methods, the model can accurately identify the operating state of the electricity–heat energy system at any time section.
研究综合能源系统的实时状态监测和识别,及时掌握其状态以实现稳定运行是非常有意义的。本文提出了一种基于多类数据均衡和极梯度提升(XGBoost)的综合能源系统状态识别方法。首先,利用拉丁超立方采样模拟不同时刻的负荷。设置不同的系统状态,并与不同时刻的模拟负荷相结合,以确定不同时刻的系统运行状态。然后,利用能量流模型计算不同状态下的系统功率流,并获得特征指标,形成原始数据集。针对数据不平衡的问题,采用超采样技术对数据进行预处理,以实现数据集的平衡。利用预处理后的数据训练 XGBoost,并基于 K 折交叉验证和网格搜索获得模型的最优超参数。最后,利用预处理数据集来验证所提出的方法。计算结果表明,识别模型的准确率达到了 87.79%。与传统方法相比,该模型能准确识别任意时间段的电-热能源系统运行状态。