Short term load forecasting (STLF) using artificial neural network based multiple lags of time series

• Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
• Main Author: Harun M.H.H.; Othman M.M.; Musirin I.
• Format: Conference paper
• Language: English
• Published: 2009
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349089000&doi=10.1007%2f978-3-642-03040-6_54&partnerID=40&md5=2b8516de31620baa3a90cd1480a8da0e
• ISSN: 16113349
• DOI: 10.1007/978-3-642-03040-6_54

This paper presents the artificial neural network (ANN) that used to perform the short-term load forecasting (STLF). The input data of ANN is comprises of multiple lags of hourly peak load. Hence, imperative information regarding to the movement patterns of a time series can be obtained based on the multiple time lags of chronological hourly peak load. This may assist towards the improvement of ANN in forecasting the hourly peak loads. The Levenberg-Marquardt optimization technique is used as a back propagation algorithm for the ANN. The Malaysian hourly peak loads are used as a case study in the estimation of STLF using ANN. The results have shown that the proposed technique is robust in forecasting the future hourly peak loads with less error. © 2009 Springer Berlin Heidelberg.