Short-term Load Forecasting Based on Chaotic Time Series and Neural Networks

A method of short-term load forecasting based on chaotic time series and neural networks is presented in this paper.Firstly,attractors in phase spaces using chaotic theory is reconstructed.Secondly,the attractor's evolvement using BP neural networks is made,and the neural network's input data using Euclid distance is selected.The result analysis of the practical examples show that the proposed method is effective and feasible.