Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/62759
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dc.titleShort term load forecasting using genetic algorithm and neural networks
dc.contributor.authorHeng, Edmund T.H.
dc.contributor.authorSrinivasan, Dipti
dc.contributor.authorLiew, A.C.
dc.date.accessioned2014-06-17T06:54:33Z
dc.date.available2014-06-17T06:54:33Z
dc.date.issued1998
dc.identifier.citationHeng, Edmund T.H.,Srinivasan, Dipti,Liew, A.C. (1998). Short term load forecasting using genetic algorithm and neural networks. Proceedings of the International Conference on Energy Management and Power Delivery, EMPD 2 : 576-581. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/62759
dc.description.abstractThis paper presents an Artificial Neural Network (ANN) model trained by a genetic algorithm (GA) for short term load forecasting. Genetic algorithms (GAs) seek to solve optimization problems using the methods of evolution, specifically survival of the fittest. The software, Genehunter from Ward Systems Group was used to build a ANN model capable of forecasting one-day ahead hourly loads for weekdays and weekends. The proposed model is a three-layered feedforward backpropagation network. The results indicate that this model can achieve greater forecasting accuracy than the traditional statistical model. This ANN model has been implemented on real load data for the year 1995.
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentELECTRICAL ENGINEERING
dc.description.sourcetitleProceedings of the International Conference on Energy Management and Power Delivery, EMPD
dc.description.volume2
dc.description.page576-581
dc.description.coden00235
dc.identifier.isiutNOT_IN_WOS
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