Short-Term Power-Generation Prediction of High Humidity Island Photovoltaic Power Station Based on a Deep Hybrid Model

Jiahui Wang, Mingsheng Jia, Shishi Li, Kang Chen, Cheng Zhang, Xiuyu Song and Qianxi Zhang ()
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Jiahui Wang: College of Ocean Engineering and Energy, Guangdong Ocean University, Zhanjiang 524088, China
Mingsheng Jia: College of Ocean Engineering and Energy, Guangdong Ocean University, Zhanjiang 524088, China
Shishi Li: College of Ocean Engineering and Energy, Guangdong Ocean University, Zhanjiang 524088, China
Kang Chen: College of Ocean Engineering and Energy, Guangdong Ocean University, Zhanjiang 524088, China
Cheng Zhang: College of Ocean Engineering and Energy, Guangdong Ocean University, Zhanjiang 524088, China
Xiuyu Song: Beijing Jingneng Clean Energy Co., Limited, Zhanjiang 524088, China
Qianxi Zhang: College of Ocean Engineering and Energy, Guangdong Ocean University, Zhanjiang 524088, China

Sustainability, 2024, vol. 16, issue 7, 1-24

Abstract: Precise prediction of the power generation of photovoltaic (PV) stations on the island contributes to efficiently utilizing and developing abundant solar energy resources along the coast. In this work, a hybrid short-term prediction model (ICMIC-POA-CNN-BIGRU) was proposed to study the output of a fishing–solar complementary PV station with high humidity on the island. ICMIC chaotic mapping was used to optimize the initial position of the pelican optimization algorithm (POA) population, enhancing the global search ability. Then, ICMIC-POA performed hyperparameter debugging and L2-regularization coefficient optimization on CNN-BIGRU (convolutional neural network and bidirectional gated recurrent unit). The L2-regularization technique optimized the loss curve and over-fitting problem in the CNN-BIGRU training process. To compare the prediction effect with the other five models, three typical days (sunny, cloudy, and rainy) were selected to establish the model, and six evaluation indexes were used to evaluate the prediction performance. The results show that the model proposed in this work shows stronger robustness and generalization ability. K-fold cross-validation verified the prediction effects of three models established by different datasets for three consecutive days and five consecutive days. Compared with the CNN-BIGRU model, the RMSE values of the newly proposed model were reduced by 64.08%, 46.14%, 57.59%, 60.61%, and 34.04%, respectively, in sunny, cloudy, rainy, continuous prediction 3 days, and 5 days. The average value of the determination coefficient R 2 of the 20 experiments was 0.98372 on sunny days, 0.97589 on cloudy days, and 0.98735 on rainy days.

Keywords: short-term photovoltaic forecasting; pelican optimization algorithm; ICMIC chaotic mapping; CNN-BIGRU; L2 regularization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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