Short-Term Load Forecasting Based On Self-Organizing Fuzzy Neural Networks

Short-term load forecasting has become increasingly important since the rise of the competitive energy markets and has become one of the major areas of research in recent years. Toward to this important topic, this paper proposes a new approach- the self-organizing fuzzy neural network (SOFNN) modeling method for the short-term load forecasting. The main advantages of this approach are that, firstly, it is very user friendly as SOFNN can automatically determine the model structure and identify the model parameters without requiring the in-depth knowledge about fuzzy systems and neural networks; secondly, it provides the excellent forecasting accuracy. Applying this approach based on the real data, the achieved average mean absolute percentage error (MAPE) for load forecasting is less than 1%.