Clustering based Short Term Load Forecasting using Support Vector Machines

A novel clustering based short term load forecasting (STLF) using support vector machines (SVM) is presented in this paper. The forecasting is performed for the 48 half hourly loads of the next day. The daily average load of each day for all the training patterns and testing patterns is calculated and the patterns are clustered using a threshold value between the daily average load of the testing pattern and the daily average load of the training patterns. The data considered for forecasting contains 2 years of half hourly daily load and daily average temperature. The proposed architecture is implemented in Matlab. The results obtained from clustering the input patterns and without clustering are presented and the results show that the clustering based approach is more accurate.