Combined use of unsupervised and supervised learning for daily peak load forecasting

Abstract

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 developing a FFNN that uses the un-clustered data. The results proved the superiority and effectiveness of the PHM. Linear regression (LR) models have also been developed for PLF, and the results indicated that the PHM produces considerably better forecasts than those of LR models. Furthermore, the results show that the suggested clustering approach significantly improves the forecasting results on regression analysis too.