Short-term photovoltaic power prediction based on EMD-GWO-RBF

Jiaqi Wang; Hui Xiao; Weiji Deng; Maolin Zhang

doi:10.1117/12.2639968

19 October 2022 Short-term photovoltaic power prediction based on EMD-GWO-RBF

Jiaqi Wang, Hui Xiao, Weiji Deng, Maolin Zhang

Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122945N (2022) https://doi.org/10.1117/12.2639968
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China

Abstract

This paper presents a short-term photovoltaic power prediction method based on EMD-GWO-RBF. Aiming at the nonlinearity and strong randomness of photovoltaic output, empirical mode decomposition (EMD) is used to decompose the historical photovoltaic power data into multiple modal component sequences with different regularity. In addition, the radial basis function neural network model optimized by Gray Wolf algorithm (GWO) is constructed for prediction respectively. Finally, the prediction results are superimposed to obtain the optimal prediction results. Based on the photovoltaic power and meteorological data of Zhujia photovoltaic power station in Hunan, the algorithm is tested for different weather. The results show that the proposed model has high prediction accuracy under different weather conditions.

Citation Download Citation

Jiaqi Wang, Hui Xiao, Weiji Deng, and Maolin Zhang "Short-term photovoltaic power prediction based on EMD-GWO-RBF", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122945N (19 October 2022); https://doi.org/10.1117/12.2639968

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