Fuzzy regression as an additive tool for short term load forecasting

So far, many studies on the load forecasting have been made to improve the prediction accuracy using various methods such as regression, artificial neural network (ANN) and neural network-fuzzy methods. In order to reduce the load forecasting error, the concept of fuzzy regression analysis is employed to load forecasting problem. The difference between regressed forecasting results and actual results is analyzed using fuzzy concept. Error correction was then applied based on fuzzy sense of error involved in prediction. Forecasted results need expert inference for which fuzzy proves to be beneficial. Results analyzed clearly support the viewpoint. The fuzzy linear regression model is made from the load data of the previous years and the coefficients of the model are found by solving the mixed linear programming problem. The fuzzy correction in predicted results improved the error from 1 to 3 %.