Short-term power prediction of distributed photovoltaic based on CNN-LSTM and meteorological interpolation

Fengchao Chen; Junni Su; Qiwei Li; Mengyong Duan; Zheng Liu; Hua Zheng

doi:10.1117/12.2685707

19 October 2023 Short-term power prediction of distributed photovoltaic based on CNN-LSTM and meteorological interpolation

Fengchao Chen, Junni Su, Qiwei Li, Mengyong Duan, Zheng Liu, Hua Zheng

Author Affiliations +

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 1270958 (2023) https://doi.org/10.1117/12.2685707
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

The photovoltaic power generation has strong randomness and fluctuation. Considering the traditional photovoltaic power prediction methods mainly focus on centralized photovoltaic and rely heavily on meteorological data, while distributed photovoltaic usually does not have perfect meteorological data, so the existing photovoltaic power prediction methods are difficult to apply to distributed photovoltaic. Based on the known wide-area meteorological resource data, the model using the Kriging method and the inverse distance weighting (IDW) method to interpolate the meteorological data of the of the distributed photovoltaic power stations by its longitude and latitude positions, using the CNN network to extract the hidden feature information, and constructs a prediction model based on LSTM. The model verifies the effectiveness of the method through actual data.

Citation Download Citation

Fengchao Chen, Junni Su, Qiwei Li, Mengyong Duan, Zheng Liu, and Hua Zheng "Short-term power prediction of distributed photovoltaic based on CNN-LSTM and meteorological interpolation", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 1270958 (19 October 2023); https://doi.org/10.1117/12.2685707

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