Short-term load forecasting with fuzzy regression tree in power systems

This paper proposes a hybrid method for short-term load forecasting in power systems. Short-term load forecasting is one of the most important problems in power system operation and planning. Therefore, more accurate models are required to handle it appropriately. The proposed method is based on the fuzzy regression tree of a data mining method and the multi-layer perceptron (MLP) of artificial neural networks. The fuzzy regression tree works to discover important rules from actual data and classify input data into some classes. On the other hand, MLP is used to predict one-step ahead loads. This paper aims to clarify the nonlinear relationship between input and output variables. In this paper, to enhance the accuracy of the regression tree, simplified fuzzy inference is introduced to determine the split values. The proposed method is successfully applied to real data