{"title":"Application of an intelligent system based on EPSO and ANFIS to price forecasting","authors":"J. Catalão, G. Osório, H. Pousinho","doi":"10.1109/ISAP.2011.6082232","DOIUrl":null,"url":null,"abstract":"This paper proposes evolutionary particle swarm optimization (EPSO) combined with an adaptive-network-based fuzzy inference system (ANFIS) for short-term electricity prices forecasting. In a deregulated framework, producers and consumers require short-term price forecasting to derive their bidding strategies to the electricity market. Accurate forecasting tools are required for producers to maximize their profits and for consumers to maximize their utilities. The accuracy of the price forecasting attained with the proposed intelligent system is thoroughly evaluated, reporting the numerical results from a real-world case study.","PeriodicalId":424662,"journal":{"name":"2011 16th International Conference on Intelligent System Applications to Power Systems","volume":"85 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 16th International Conference on Intelligent System Applications to Power Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISAP.2011.6082232","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
This paper proposes evolutionary particle swarm optimization (EPSO) combined with an adaptive-network-based fuzzy inference system (ANFIS) for short-term electricity prices forecasting. In a deregulated framework, producers and consumers require short-term price forecasting to derive their bidding strategies to the electricity market. Accurate forecasting tools are required for producers to maximize their profits and for consumers to maximize their utilities. The accuracy of the price forecasting attained with the proposed intelligent system is thoroughly evaluated, reporting the numerical results from a real-world case study.