{"title":"A SOM neural network approach to load forecasting. Meteorological and time frame influence","authors":"M. López, S. Valero, C. Senabre, J. Aparicio","doi":"10.1109/POWERENG.2011.6036553","DOIUrl":null,"url":null,"abstract":"An artificial neural network based on Kohonen self-organizing maps (SOM) and its application to short-term load forecasting (STLF) is presented. The proposed model is capable of forecasting up to 24 hour long profiles, up to 24 hours ahead of the beginning of the period. The input used by the model depends on the available information at the time of the forecast, and it may contain meteorological variables and previous hourly load values. Also, different time frames for the input training data are analyzed. The output of the model is a curve of the forecasted load for the specified period. The test of forecasting 2009 data from the Spanish power system resulted in a 2.67% MAPE (mean absolute percentage error).","PeriodicalId":166144,"journal":{"name":"2011 International Conference on Power Engineering, Energy and Electrical Drives","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Power Engineering, Energy and Electrical Drives","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/POWERENG.2011.6036553","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
An artificial neural network based on Kohonen self-organizing maps (SOM) and its application to short-term load forecasting (STLF) is presented. The proposed model is capable of forecasting up to 24 hour long profiles, up to 24 hours ahead of the beginning of the period. The input used by the model depends on the available information at the time of the forecast, and it may contain meteorological variables and previous hourly load values. Also, different time frames for the input training data are analyzed. The output of the model is a curve of the forecasted load for the specified period. The test of forecasting 2009 data from the Spanish power system resulted in a 2.67% MAPE (mean absolute percentage error).
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