Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/62759
Title: Short term load forecasting using genetic algorithm and neural networks
Authors: Heng, Edmund T.H.
Srinivasan, Dipti 
Liew, A.C. 
Issue Date: 1998
Source: 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.
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.
Source Title: Proceedings of the International Conference on Energy Management and Power Delivery, EMPD
URI: http://scholarbank.nus.edu.sg/handle/10635/62759
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