A Hybrid Algorithm for Short-Term Wind Power Prediction

Accurate and effective wind power prediction plays an important role in wind power generation, distribution, and management. Inthis paper, a hybrid algorithm based on gradient descent and meta-heuristic optimization is designed to improve the accuracy of prediction and reduce the computational burden. The hybrid algorithm includes three steps: in the first step, we use the gradient descent algorithm to get the initial parameters. Secondly, we input the initial parameters into the meta-heuristic optimization algorithm to search for the best parameters (high-quality inferior solutions). Finally, we input optimized parameters into the RMSProp optimization algorithm and conduct gradient descent again to find a better solution. We used 2021 wind power data from Guangxi, China for the experiment. The results show that the hybrid prediction algorithm has better performance than the traditional Back Propagation (BP) in accuracy, stability, and efficiency.

Automated summary

In this paper, a hybrid algorithm based on gradient descent and meta-heuristic optimization is proposed to enhance the accuracy of wind power prediction and reduce computational burden. The experimental results demonstrate that the hybrid algorithm outperforms the traditional Back Propagation (BP) algorithm in terms of accuracy, stability, and efficiency.