Wind power prediction based on PSO-Kalman

• DOI: 10.1016/j.egyr.2022.02.077
• Publication Date: 2022-02-18T10:36:46Z
• Authors: Daoqing Li; Xiaodong Yu; Shulin Liu; Xia Dong; Hongzhi Zang; Rui Xu
• Keywords: Prediction interval; Kernel density estimation

Because of its clean and green, wind power is broadly used all over the world. Wind random unstable, so integration will inevitably bring great impact to system. Accurate prediction can effectively alleviate caused by uncertainty. In order increase accuracy prediction, this article uses paper swarm optimization algorithm (PSO) improve traditional Kalman filter, PSO-Kalman point model established. The proposed solves problem low filter observation noise process noise. Finally, based on error, non-parametric kernel density estimation for interval prediction. By experimental simulation, comparing error evaluation indexes it be found that smallest, indicating PSO Kalman. On basis, performance also better than before. Moreover, in converges fast has general applicability.