Short-term PV power data prediction based on improved FCM with WTEEMD and adaptive weather weights

Photovoltaic (PV) systems are commonly used in zero energy buildings(ZEBs) due to their high efficiency and convenience. However, PV systems are affected by meteorological factors with nonlinearities and stochasticity in the power generation process, which leads to inaccurate prediction of PV power, affects the relationship between energy supply and user demand, and then has a significant impact on the stability of PV grid connection. To address these challenges, this paper proposes a WTEEMD-FCM-IGWO-LSTM method for the numerical prediction of PV power. Firstly, for the noise interference in the meteorological data of PV power generation collected from PV power stations, this paper proposes an Ensemble Empirical Mode Decomposition (EEMD) method based on the wavelet threshold algorithm improvement (WTEEMD) for noise reduction and reconstruction of meteorological data to enhance the signal-to-noise ratio in the data. Secondly, considering the stochastic nature of unstable meteorological factors, an improved fuzzy C-mean clustering (FCM) method is employed to classify the PV power process dataset and enhance the correlation between meteorological factors and power data. Subsequently, prediction models for the power values of the PV power generation process under each of the three meteorological conditions are developed using Long Short-Term Memory (LSTM) based on the classified sample data. Finally, the proposed model is evaluated through simulation using historical Australian PV data. The experimental results show that it is possible to accurately predict the power of PV power generation, improve the utilization of clean energy, and support the sustainable development of ZEBs.