A hybrid neural genetic method for load forecasting based on phase space reconstruction

Purpose - The purpose of this paper is to improve forecasting accuracy for short-term load series. Design/methodology/approach - A forecasting method based on chaotic time series and optimal diagonal recurrent neural networks (DRNN) is presented. The input of the DRNN is determined by the embedding dimension of the reconstructed phase space, and adaptive dynamic back propagation (DBP) algorithm is used to train the network. The connection weights of the DRNN are optimized via modified genetic algorithms, and the best results of optimization are regarded as initial weights for the network. The new method is applied to predict the actual short-term load according to its chaotic characteristics, and the forecasting results also validate the feasibility. Findings - For the chaos time series, the hybrid neural genetic method based on phase space reconstruction can carry out the short-term prediction with the higher accuracy. Research limitations/implications The proposed method is not suited to medium and long-term load forecasting. Practical implications - The accuracy of the load forecasting is important to the economic and secure operation of power systems; also, the neural genetic method can improve forecasting accuracy. Originality/value - This paper will help overcome the defects of traditional neural network and make short-term load forecasting more accurate and fast.