{"title":"Recurrent neural networks and load forecasting","authors":"J. Connor, L. Atlas, D. Martin","doi":"10.1109/ANN.1991.213491","DOIUrl":null,"url":null,"abstract":"The ability of a recurrent network to model load forecasting is investigated. Its performance in a competition is then contrasted with that of feedforward networks and linear models. Its weaknesses and strengths are then analyzed to give guidelines to the design of neural net predictors with the hope of designing better predictors in the future.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ANN.1991.213491","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18
Abstract
The ability of a recurrent network to model load forecasting is investigated. Its performance in a competition is then contrasted with that of feedforward networks and linear models. Its weaknesses and strengths are then analyzed to give guidelines to the design of neural net predictors with the hope of designing better predictors in the future.<>
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