COMPARATIVE STUDY OF FUZZY LOGIC AND ANN FOR SHORT TERM LOAD FORECASTING

Load forecasting is an important component for power system energy management system. Precise load forecasting helps the electric utility to make unit commitment decisions, reduce spinning reserve capacity and schedule device maintenance plan properly and it also reduces the generation cost and increases reliability of power systems. In this work, a fuzzy logic as well as artificial neural network approach for short term load forecasting is attempted. Time, temperature and similar previous day load are used as the independent variables for short term load forecasting. Based on the time, temperature and similar previous day load, fuzzy rule base are prepared using mamdani implication, which are eventually used for the short term load forecasting. Similarly, monthly load data along with forecasted temperature are used to train the neural network. MATLAB SIMULINK software is used here in this work. For the short term load forecasting, load data from the specific area load dispatch center is considered