%0 Journal Article
%T A Taguchi and Neural Network Based Electric Load Demand Forecaster
%A Albert W. L. Yao
%A H. T. LiaoC. Y. Liu
%J The Open Automation and Control Systems Journal
%D 2008
%I 
%R 10.2174/1874444300801010007]
%X In this paper, we present Taguchi¡¯s and rolling modeling methods of artificial neural network (ANN) for very-short-term electric demand forecasting (VSTEDF) from the consumers¡¯ viewpoint. The rolling model is a metabolism technique that guarantees input data are always the most recent values. In ANN prediction, several factors that may influence the model should be well examined. Taguchi¡¯s method was employed to optimize the parameter settings for the ANN-based electric demand-value forecaster. Our experimental result shows that the optimal settings of ANN prediction model are 3 lagged load points, 0.1 for the momentum, 5 hidden neurons and 0.1 for the learning rate. The error of forecasting is as small as 3%. That is, comparison with the results of ordinary ANN and Grey prediction, the presented Taguchi-ANN-based forecaster gives more accurate prediction for VSTEDF.
%U http://www.benthamscience.com/open/toautocj/articles/V001/7TOAUTOCJ.htm