ELECTRICITY LOAD FORECASTING BY ARTIFICIAL NEURAL NETWORK MODEL USING WEATHER DATA

Abstract

This paper discusses significant role of advanced technique in short-term load forecasting (STLF), that is, the forecast of the power system load over a period ranging from one hour to one week. An adaptive neuro - wavelet time series forecast model is adopted to perform STLF. The model is composed of several neural networks (NN) whose data are processed using a wavelet technique. The data to be used in the model are both the temperature and electricity load historical data. The temperature variable is included because temperature has a close relationship with electricity load. The calculation of mean average percentage error for a specific region under study in India is done and results obtained using MATLAB’S ANN toolbox. This study proposes a STLF model with a high forecasting accuracy. In this study absolute mean error (AME) value calculated is 1.24% which represents a reasonable degree of accuracy.