Electricity Price Forecasting Method Based on Quantum Immune Optimization BP Neural Network Algorithm

Abstract

This paper presents electricity price forecasting method based on quantum immune optimization Back Propagation (BP) neural network algorithm. The prediction model of electric price can be constructed with BP neural network algorithm, however, the BP neural network is readily trapped in local optimal in the electricity price prediction. With this regard, based on the quantum immune optimization algorithm, a modified BP neural network price prediction method is proposed. A realistic New Zealand power company is used to test the proposed algorithm, the numerical results show that, compared the traditional BP neural network, the proposed quantum immune optimization BP algorithm has much higher accuracy in the prediction of electricity price. Thus, it is a better and more practical pricing prediction method and has better actual prediction effect. And it also demonstrates that this optimization algorithm not only greatly improves the accuracy of electricity price prediction, but also makes the prediction process faster and more efficient, which can effectively reduce errors and shorten the prediction period.