Wind Energy Output Prediction Model Based on DPSO-BP Neural Network

Abstract

Wind Energy Output Prediction (WEOP) has an important impact on the integration of wind energy systems in the power grid, the management, power systems dispatching, safe and stable operation. Short-term probabilistic WEOP is a perfect choice to increase the stability of the power grid. Still, it has a high error because of uncertainty factors such as wind speed, and very important to find out a method to increase the fineness of predicting. Therefore, in this paper, the main contribution is to design an intelligent model capable of wind energy generation prediction. This aim is achieved by adopting a new Developed Particle Swarm optimization algorithm based on proposes a new inertia weight called DPSO and Back-Propagation Neural Network (DPSO-BPNN). The proposed models were compared and verified through MATLAB software test to prove the superiority over other models. The obtained results of the proposed model show a fast convergence and high prediction accuracy. Also, the error rate was improved compared with the published research by up to 98%.