Photovoltaic power prediction model based on EMD-KPCA-GRU

Huazhi Chi; He Jiang; Yan Zhao

doi:10.1117/12.3024444

13 May 2024 Photovoltaic power prediction model based on EMD-KPCA-GRU

Huazhi Chi, He Jiang, Yan Zhao

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 1315908 (2024) https://doi.org/10.1117/12.3024444
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

With the aim of enhancing the accuracy of PV power forecasting, a PV power prediction model has been presented based on the EMD-PKCA-GRU neural network. Firstly, the sequence of four meteorological factors constraining the output power of photovoltaic power generation is decomposed using the empirical model decomposition method. The nonstationary nature of the meteorological factor sequences is reduced. Next, feature extraction is conducted from decomposed sequences using kernel principal component analysis (KPCA) methods. This eliminates the redundancy of the original sequence and reduces the dimensionality of the model input. Finally, the Gated Recurrent Unit (GRU) is established, using the GRU to predict PV power generation. The validation was conducted using the 2021 data from the Trina 1B power station in the DKASC dataset from Australia. The outcome shows that the prediction accuracy is higher than the traditional model.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Huazhi Chi, He Jiang, and Yan Zhao "Photovoltaic power prediction model based on EMD-KPCA-GRU", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 1315908 (13 May 2024); https://doi.org/10.1117/12.3024444

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