Rooftop PV Power Prediction Model based on Data Reconstruction and CEEMDAN

In order to improve the prediction accuracy of rooftop photovoltaic (PV) power, a PV power prediction method based on data reconstruction and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is proposed, taking into account the lack of rooftop PV power. Firstly, the power relationship between rooftop PV and adjacent centralized PV is established based on daily power generation to reconstruct rooftop PV power. Subsequently, considering the difference in power generation performance between rooftop PV and adjacent centralized PV, the relative generation efficiency is defined to correct reconstructed value. Finally, the rooftop PV power prediction model is established based on CEEMDAN and long short-term memory network (LSTM). The operation data acquired from PV power station in northern China are considered as an example to verify the effectiveness of the proposed method. The experimental results show that the proposed method has a high prediction accuracy under different weather types.