A theory-guided deep-learning method for predicting power generation of multi-region photovoltaic plants. (February 2023)

• Record Type: Journal Article
• Authors: Du, Jian; Zheng, Jianqin; Liang, Yongtu; Liao, Qi; Wang, Bohong; Sun, Xu; Zhang, Haoran; Azaza, Maher; Yan, Jinyue
• Is Part Of: Engineering applications of artificial intelligence. Volume 118(2023)
• Publication Date: 2023-02
• Subjects: Photovoltaic power generation prediction -- Local dependency -- Time series -- Multi-region -- TG-A-CNN-LSTM -- Theory guided; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105647
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Recently, clean solar energy has aroused wide attention due to its excellent potential for electricity production. A highly accurate prediction of photovoltaic power generation (PVPG) is the basis of the production and transmission of electricity. However, the current works neglect the regional correlation characteristics of PVPG and few studies propose an effective framework by incorporating prior knowledge for more physically reasonable results. In this work, a hybrid deep learning framework is proposed for simultaneously capturing the spatial correlations among different regions and temporal dependency patterns with various importance. The scientific theory and domain knowledge are incorporated into the deep learning model to make the predicted results possess physical reasonability. Subsequently, the theory-guided and attention-based CNN-LSTM (TG-A-CNN-LSTM) is constructed for PVPG prediction. In the training process, data mismatch and boundary constraint are incorporated into the loss function, and the positive constraint is utilized to restrict the output of the model. After receiving the parameters of the neural network, a TG-A-CNN-LSTM model, whose predicted results obey the physical law, is constructed. A real energy system in five regions is used to verify the accuracy of the proposed model. The predicted results indicate that TG-A-CNN-LSTM can achieve higher precision of PVPG prediction than other prediction models, with RMSE being 11.07, MAE being 4.98, and R 2 being 0.94, respectively. Moreover, the performance of prediction models with sparse data is tested to illustrate the stability and robustness of TG-A-CNN-LSTM. Highlights: The weight-based temporal-spatial network is utilized to capture variables correlations features. Scientific theory is incorporated for photovoltaic power generation predicting with physical reasonability. A multi-region real power system is utilized to illustrate the superiority of the proposed model. The proposed model outperforms other methods in predicting photovoltaic power generation with sparse data.