基于混合灰色关联分析-广义回归神经网络的光伏电站短期功率预测

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Abstract With the large-scale photovoltaic (PV) power generation connected to the grid, the randomness and volatility of its output brings great challenges to the dispatching management of the grid. Therefore, a hybrid (grey relational analysis and generalized regression neural network, GRA-GRNN) prediction model considering both statistical (historical PV output power) and physical (historical and future meteorological information) variables is proposed. Firstly, the (Pearson correlation coefficient, R²) between multivariate meteorological factors and PV power is calculated, and the meteorological factors with higher correlation coefficient are selected as the meteorological input factors for the establishment of the prediction model. Secondly, the GRA algorithm is applied to calculate the correlation degree between the historical days and the forecasting day to determine the optimal similarity day. Then, the PV power and meteorological input factors of the optimal similarity day and the relevant meteorological parameters of the forecasting day are selected as the input parameters of the GRNN model, and the predicted output power at each time of the forecasting day is obtained. Finally, the designed model is trained and tested by using the historical meteorological datasets and power datasets of a PV power plant provided by the (desert knowledge australia solar center, DAKSC) website to verify the performance of the model in different seasons. The results show that the proposed model is superior to the selected comparison models.

Key words： photovoltaic power prediction grey relational analysis generalized regression neural network optimal similarity day

Received: 13 March 2019 Published: 29 September 2019

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	Articles by authors
	Peng Zhouning
	Lin Peijie
	Lai Yunfeng
	Cheng Shuying
	Chen Zhicong

Cite this article:

Peng Zhouning,Lin Peijie,Lai Yunfeng等. Short-term power prediction for photovoltaic power plants based on hybrid grey relational analysis-generalized regression neural network[J]. Electrical Engineering, 2019, 20(10): 11-18.

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http://dqjs.cesmedia.cn/EN/Y2019/V20/I10/11

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