TCN-GRU Based on Attention Mechanism for Solar Irradiance Prediction

Zhi Rao, Zaimin Yang, Xiongping Yang, Jiaming Li, Wenchuan Meng and Zhichu Wei ()
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Zhi Rao: School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
Zaimin Yang: Energy Development Research Institute, China Southern Power Grid, Guangzhou 510530, China
Xiongping Yang: China Southern Power Grid, Guangzhou 510623, China
Jiaming Li: China Southern Power Grid, Guangzhou 510623, China
Wenchuan Meng: Energy Development Research Institute, China Southern Power Grid, Guangzhou 510530, China
Zhichu Wei: School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China

Energies, 2024, vol. 17, issue 22, 1-17

Abstract: The global horizontal irradiance (GHI) is the most important metric for evaluating solar resources. The accurate prediction of GHI is of great significance for effectively assessing solar energy resources and selecting photovoltaic power stations. Considering the time series nature of the GHI and monitoring sites dispersed over different latitudes, longitudes, and altitudes, this study proposes a model combining deep neural networks and deep convolutional neural networks for the multi-step prediction of GHI. The model utilizes parallel temporal convolutional networks and gate recurrent unit attention for the prediction, and the final prediction result is obtained by multilayer perceptron. The results show that, compared to the second-ranked algorithm, the proposed model improves the evaluation metrics of mean absolute error, mean absolute percentage error, and root mean square error by 24.4%, 33.33%, and 24.3%, respectively.

Keywords: photovoltaic prediction; photovoltaic power plants site selection; deep neural networks; deep convolutional neural networks; map distribution characteristics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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