Short term load forecasting by clustering technique based on daily average and peak loads

Abstract

A novel clustering based Short Term Load Forecasting (STLF) using Artificial Neural Network (ANN) for forecasting the next day load is presented in this paper. The input parameters considered for prediction are load, temperature and day of the week. 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. Similarly, the training patterns are clustered using a threshold value between the daily peak load of the testing pattern and the daily peak load of the training pattern. The ANN is trained with Back Propagation Algorithm and tested. The results for different cases - without clustering, clustering based on daily average load, clustering based on daily peak load are presented and the results show that clustering technique provide better results.