基于LS-SVM的短期电力负荷预测

2010 International Conference of Information Science and Management Engineering Pub Date : 2010-08-07 DOI:10.1109/ISME.2010.86

L. Bin, Xuefeng Guang

{"title":"基于LS-SVM的短期电力负荷预测","authors":"L. Bin, Xuefeng Guang","doi":"10.1109/ISME.2010.86","DOIUrl":null,"url":null,"abstract":"In order to solve the Short-term Load Forecasting problems in Power Systems, this article puts forward the Least Squares Support Vector Machine’s improved model by selecting the appropriate Gauss kernel function and proposing the error calculation analytical method, thus reduces the computational complicate problems when large amount of data is input in Short-term Power Load Forecasting. An example is given to prove the validity of the algorithm.","PeriodicalId":348878,"journal":{"name":"2010 International Conference of Information Science and Management Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Short-Term Power Load Forecasting Based on LS-SVM\",\"authors\":\"L. Bin, Xuefeng Guang\",\"doi\":\"10.1109/ISME.2010.86\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to solve the Short-term Load Forecasting problems in Power Systems, this article puts forward the Least Squares Support Vector Machine’s improved model by selecting the appropriate Gauss kernel function and proposing the error calculation analytical method, thus reduces the computational complicate problems when large amount of data is input in Short-term Power Load Forecasting. An example is given to prove the validity of the algorithm.\",\"PeriodicalId\":348878,\"journal\":{\"name\":\"2010 International Conference of Information Science and Management Engineering\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference of Information Science and Management Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISME.2010.86\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference of Information Science and Management Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISME.2010.86","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 4

摘要

为了解决电力系统短期负荷预测问题，本文通过选择合适的高斯核函数，提出了最小二乘支持向量机的改进模型，并提出了误差计算分析方法，从而减少了短期负荷预测中输入大量数据时的计算复杂性问题。最后通过实例验证了该算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Short-Term Power Load Forecasting Based on LS-SVM

In order to solve the Short-term Load Forecasting problems in Power Systems, this article puts forward the Least Squares Support Vector Machine’s improved model by selecting the appropriate Gauss kernel function and proposing the error calculation analytical method, thus reduces the computational complicate problems when large amount of data is input in Short-term Power Load Forecasting. An example is given to prove the validity of the algorithm.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2010 International Conference of Information Science and Management Engineering

自引率

0.00%

发文量