《Combined use of unsupervised and supervised learning for daily peak load forecasting》.pdf

Available online at Energy Conversion and Management 49 (2008) 1302–1308 /locate/enconman Combined use of unsupervised and supervised learning for daily peak load forecasting M.R. Amin-Naseri *, A.R. Soroush Department of Industrial Engineering, Tarbiat Modares University, Jalal Alahmad Highway, P.O. Box 14115-143, Tehran, Iran Received 22 May 2007; accepted 6 January 2008 Available online 10 March 2008 Abstract In this paper, we have aimed to present a hybrid neural network model for daily electrical peak load forecasting (PLF). Since peak loads usually follow similar patterns, classification of data improves the accuracy of the forecasts. Several factors in peak load, e.g. weather temperature, relative humidity, wind speed and cloud cover, were introduced into the model in order to enhance forecast quality. Most classification attempts in the literature have been intuitive and empty of justification. In this paper, we have proposed a novel approach for clustering data by using a self-organizing map. The Davies–Bouldin validity index was introduced to determine the best clusters. A feed forward neural network (FFNN) has been developed for each cluster to provide the PLF. Eight training algorithms have also been used in order to train the proposed FFNNs. Applying principal component analysis (PCA) decreased the dimensions of the network’s inputs and led to simpler architecture. To evaluate the effectiveness of the proposed hybrid model (PHM), forecasting has been performed by dev