{"title":"Short-Term Power Load Forecasting Based on Fuzzy-RBF Neutral Network","authors":"Jia Zheng-yuan, Tian Li","doi":"10.1109/ICRMEM.2008.41","DOIUrl":null,"url":null,"abstract":"The paper proposes short-term power load forecasting model based on fuzzy RBF neural network, it has overcome the BP algorithm's disadvantage of slow convergence rate and it fall into partially the smallest insufficiency easily. RBF network model in the use of the latest neighborhood clustering algorithm, and the network structure and the parameters are double-adjusted and the training speed and forecast accuracy are improved. The examples also show that the model can improve forecast accuracy effectively, reducing the error of load forecasting, and the inherent defects of BP neural network have been avoid.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Conference on Risk Management & Engineering Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICRMEM.2008.41","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
The paper proposes short-term power load forecasting model based on fuzzy RBF neural network, it has overcome the BP algorithm's disadvantage of slow convergence rate and it fall into partially the smallest insufficiency easily. RBF network model in the use of the latest neighborhood clustering algorithm, and the network structure and the parameters are double-adjusted and the training speed and forecast accuracy are improved. The examples also show that the model can improve forecast accuracy effectively, reducing the error of load forecasting, and the inherent defects of BP neural network have been avoid.