Please use this identifier to cite or link to this item:
|dc.title||Short term load forecasting using genetic algorithm and neural networks|
|dc.contributor.author||Heng, Edmund T.H.|
|dc.identifier.citation||Heng, 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.description.abstract||This 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.description.sourcetitle||Proceedings of the International Conference on Energy Management and Power Delivery, EMPD|
|Appears in Collections:||Staff Publications|
Show simple item record
Files in This Item:
There are no files associated with this item.
checked on Jun 14, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.