Application of Neural Network Model Based on Combination of Fuzzy Classification and Input Selection in Short Term Load Forecasting

In power system, short term load forecasting (STLF) is important for optimum operation planning of power generation facilities, as it affects both system reliability and fuel consumption. Computational intelligent technique for STLF has become more and more important in electric engineering since it is a useful tool for efficient planning. So the study of STLF system requires an efficient computational tool such as computational intelligence technique. In this paper, we applied the use of computational intelligent methods to short term load forecasting systems. With power systems growth and the increase in their complexity, many factors have become influential to the electric power generation and consumption. First, we use entropy theory to select relevant ones from all load influential factors. Next, considering the features of power load and reduced influential factors, we use fuzzy classification rules to divide the past load data into different network property. Then the representative historical load data samples were selected as the training set for neural network, which have the same weather characteristic as the certain forecasting day. Finally, Elman recurrent neural network (ERNN) forecasting model is constructed which is a kind of globally feed forward locally recurrent network model with distinguished dynamical characteristics. And the effectiveness of the model has been tested using practical daily load data. The simulation results show that the presented intelligent technique for load forecasting can give satisfactory results