Short-Term Load Forecasting Model For Power System Based on Complementation of Fuzzy-Rough Set Theory And BP Neural Network

It's well known that artificial neural networks (ANN) are very commonly used for load forecasting in recent years, and it is the key point to select proper factors as input variables of ANN. According to the characteristics of electric short-term load forecasting, a complementation method based on fuzzy-rough set theory and BP NN is proposed to deal with this problem in the paper. First of all, we extract the input characteristic to make the initial decision table. Secondly, in order to reduce the information losing, fuzzing up the attribute values instead of discretizing them. Finally, reduce the input parameters of ANN by using the knowledge of the fuzzy-rough set theory. In this paper, through reduction, the author just selects several parameters which are most relative to the forecasting variable to be the rational ANN input. This method takes the weather, temperature, day type .etc into account. And in the same time, it can avoid the problems which are occurred because of overfull input parameters. The testing results on a real power system show that the proposed model is feasible.