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Open AccessArticle

Ultra-Short-Term Forecasting of Photo-Voltaic Power via RBF Neural Network

by Wanxing Ma 1, Zhimin Chen 1,* and Qing Zhu 2
1
School of Electronic and Information, Shanghai Dianji University, Shanghai 201306, China
2
NARI Technology Co., Ltd., Nanjing 211106, China
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(10), 1717; https://doi.org/10.3390/electronics9101717
Received: 14 September 2020 / Revised: 10 October 2020 / Accepted: 13 October 2020 / Published: 18 October 2020
(This article belongs to the Section Power Electronics)
With the fast expansion of renewable energy systems during recent years, the stability and quality of smart grids using solar energy have been challenged because of the intermittency and fluctuations. Hence, forecasting photo-voltaic (PV) power generation is essential in facilitating planning and managing electricity generation and distribution. In this paper, the ultra-short-term forecasting method for solar PV power generation is investigated. Subsequently, we proposed a radial basis function (RBF)-based neural network. Additionally, to improve the network generalization ability and reduce the training time, the numbers of hidden layer neurons are limited. The input of neural network is selected as the one with higher Spearman correlation among the predicted power features. The data are normalized and the expansion parameter of RBF neurons are adjusted continuously in order to reduce the calculation errors and improve the forecasting accuracy. Numerous simulations are carried out to evaluate the performance of the proposed forecasting method. The mean absolute percentage error (MAPE) of the testing set is within 10%, which show that the power values of the following 15 min. can be predicted accurately. The simulation results verify that our method shows better performance than other existing works. View Full-Text
Keywords: photo-voltaic power forecasting; RBF neural network; Spearman correlation coefficient; ultra-short-term forecasting photo-voltaic power forecasting; RBF neural network; Spearman correlation coefficient; ultra-short-term forecasting
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Ma, W.; Chen, Z.; Zhu, Q. Ultra-Short-Term Forecasting of Photo-Voltaic Power via RBF Neural Network. Electronics 2020, 9, 1717.

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