Short-time wind speed prediction based on Legendre multi-wavelet neural network

As one of the most widespread renewable energy sources, wind energy is now an important part of the power system. Accurate and appropriate wind speed forecasting has an essential impact on wind energy utilisation. However, due to the stochastic and uncertain nature of wind energy, more accurate forecasting is necessary for its more stable and safer utilisation. This paper proposes a Legendre multiwavelet-based neural network model for non-linear wind speed prediction. It combines the excellent properties of Legendre multi-wavelets with the self-learning capability of neural networks, which has rigorous mathematical theory support. It learns input-output data pairs and shares weights within divided subintervals, which can greatly reduce computing costs. We explore the effectiveness of Legendre multi-wavelets as an activation function. Meanwhile, it is successfully being applied to wind speed prediction. In addition, the application of Legendre multi-wavelet neural networks in a hybrid model in decomposition-reconstruction mode to wind speed prediction problems is also discussed. Numerical results on real data sets show that the proposed model is able to achieve optimal performance and high prediction accuracy. In particular, the model shows a more stable performance in multi-step prediction, illustrating its superiority.