Manoj Baghel, Abir Ghosh, N. Singh, Ashutosh Kumar Singh
{"title":"短期电力负荷预测用SVR实现LibSVM包和Python代码","authors":"Manoj Baghel, Abir Ghosh, N. Singh, Ashutosh Kumar Singh","doi":"10.1109/UPCON.2016.7894702","DOIUrl":null,"url":null,"abstract":"Electrical load forecasting is an important topic within the electrical market which has been done by a machine learning methodology: Support Vector Machines (SVM). Load forecasting with SVM will form the non-linear relations with the parameters that have an effect on the load; additionally to the correct modeling of the load curve on weekends and holidays. The past information is used as a sample for the applying and therefore holidays associated demand as an important factor inprediction. The LibSVM package and Python codeis used for modeling the SVM. Resultsare obtainedand comparison is made for the two methods.","PeriodicalId":151809,"journal":{"name":"2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Short - term electric load forecasting using SVR implementing LibSVM package and Python code\",\"authors\":\"Manoj Baghel, Abir Ghosh, N. Singh, Ashutosh Kumar Singh\",\"doi\":\"10.1109/UPCON.2016.7894702\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Electrical load forecasting is an important topic within the electrical market which has been done by a machine learning methodology: Support Vector Machines (SVM). Load forecasting with SVM will form the non-linear relations with the parameters that have an effect on the load; additionally to the correct modeling of the load curve on weekends and holidays. The past information is used as a sample for the applying and therefore holidays associated demand as an important factor inprediction. The LibSVM package and Python codeis used for modeling the SVM. Resultsare obtainedand comparison is made for the two methods.\",\"PeriodicalId\":151809,\"journal\":{\"name\":\"2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON)\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/UPCON.2016.7894702\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UPCON.2016.7894702","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Short - term electric load forecasting using SVR implementing LibSVM package and Python code
Electrical load forecasting is an important topic within the electrical market which has been done by a machine learning methodology: Support Vector Machines (SVM). Load forecasting with SVM will form the non-linear relations with the parameters that have an effect on the load; additionally to the correct modeling of the load curve on weekends and holidays. The past information is used as a sample for the applying and therefore holidays associated demand as an important factor inprediction. The LibSVM package and Python codeis used for modeling the SVM. Resultsare obtainedand comparison is made for the two methods.