Yingjie Li, Zhining Lv, Ning Pang, Yi Luo, Gen Zhao, Jun Hu
{"title":"基于数据挖掘算法的能源互联网变压器负荷容量评估与预测新方法","authors":"Yingjie Li, Zhining Lv, Ning Pang, Yi Luo, Gen Zhao, Jun Hu","doi":"10.1109/iSPEC50848.2020.9351289","DOIUrl":null,"url":null,"abstract":"Based on realistic transformer dataset, this paper comes up with a method to predict the top oil temperature (TOT) of a main transformer based on the historic TOT, ambient temperature (AT), transformer load (TL) and present AT, TL. Technically, TOT is predicted by striking a balance between univariate time series prediction and multivariate prediction, more specifically, between considering time series features such as trend, seasonality and considering relationship among TOT, AT and TL. From the results, the proposed scheme significantly outperforms the tradition time series model and support vector regression.","PeriodicalId":403879,"journal":{"name":"2020 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"26 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A New Method to Evaluate and Predict the Load Capacity of Transformer in Energy Internet with Data Mining Algorithm\",\"authors\":\"Yingjie Li, Zhining Lv, Ning Pang, Yi Luo, Gen Zhao, Jun Hu\",\"doi\":\"10.1109/iSPEC50848.2020.9351289\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on realistic transformer dataset, this paper comes up with a method to predict the top oil temperature (TOT) of a main transformer based on the historic TOT, ambient temperature (AT), transformer load (TL) and present AT, TL. Technically, TOT is predicted by striking a balance between univariate time series prediction and multivariate prediction, more specifically, between considering time series features such as trend, seasonality and considering relationship among TOT, AT and TL. From the results, the proposed scheme significantly outperforms the tradition time series model and support vector regression.\",\"PeriodicalId\":403879,\"journal\":{\"name\":\"2020 IEEE Sustainable Power and Energy Conference (iSPEC)\",\"volume\":\"26 1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Sustainable Power and Energy Conference (iSPEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/iSPEC50848.2020.9351289\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Sustainable Power and Energy Conference (iSPEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/iSPEC50848.2020.9351289","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A New Method to Evaluate and Predict the Load Capacity of Transformer in Energy Internet with Data Mining Algorithm
Based on realistic transformer dataset, this paper comes up with a method to predict the top oil temperature (TOT) of a main transformer based on the historic TOT, ambient temperature (AT), transformer load (TL) and present AT, TL. Technically, TOT is predicted by striking a balance between univariate time series prediction and multivariate prediction, more specifically, between considering time series features such as trend, seasonality and considering relationship among TOT, AT and TL. From the results, the proposed scheme significantly outperforms the tradition time series model and support vector regression.