Wind speed forecasting by a hysteretic neural network based on Kalman filtering

The hysteretic characteristic was introduced into the activation functions of neurons, and a forward hysteretic neural network was proposed. In combination with the Kalman filter algorithm, the hysteretic neural network was applied to wind speed forecasting. A change rate series of wind speed was constructed according to the original wind speed time series. Forecasting analysis of both the series was performed with the hysteretic neural network, these prediction results were fused using the Kalman filter algorithm, and thus the optimal estimated results were obtained. Simulation results show that the hysteretic neural network has more flexible structure, better generalization ability, and better prediction performance than the conventional neural network. The prediction performance can be further improved by Kalman filter fusion.