Short-term wind power forecasting using ridgelet neural network

Rapid growth of wind power generation in many countries around the world in recent years has highlighted the importance of wind power prediction. However, wind power is a complex signal for modeling and forecasting. Despite the performed research works in the area, more efficient wind power forecast methods are still demanded. In this paper, a new prediction strategy is proposed for this purpose. The forecast engine of the proposed strategy is a ridgelet neural network (RNN) owning ridge functions as the activation functions of its hidden nodes. Moreover, a new differential evolution algorithm with novel crossover operator and selection mechanism is presented to train the RNN. The efficiency of the proposed prediction strategy is shown for forecasting of both wind power output of wind farms and aggregated wind generation of power systems.

► A new wind power forecasting strategy based on ridgelet neural networks is proposed. ► A new differential evolution algorithm for improving the training process of the ridgelet neural network is proposed. ► Numerical results for a single wind farm in Spain and the aggregated output of all wind farms in Irish power system are presented. ► Accuracy of the proposed method is compared with those of comparable techniques or industry data, showing improved performance.