An integrated frequency domain decomposition and deep neural network approach for short-term PV power forecast

Abstract

Weather disturbances and atmospheric parameters significantly influence the fluctuations in PV power output, which in turn affect the stability of grid operations. The current study proposed short-term PV power forecasting based on appropriate cutoff frequency in frequency domain and artificial intelligence method. Initially, the actual PV power data are decomposed into the frequency domain, and optimal cutoff frequency is determined by minimizing the squared difference of correlation between the decomposed components. Subsequently, the PV power is separated into low-frequency components (LFC) and high-frequency components (HFC). Then, long short-term memory (LSTM) and light gradient boosting machine (LGBM) models are then employed to forecast the LFC and HFC PV power. The final forecast output is generated using various recombination methods. The proposed combined forecast model, LFC-LGBM + HFC-LGBM, based on frequency domain decomposition (FDD) and LGBM approach, demonstrates superior performance compared to models (LFC-LSTM + HFC-LSTM), (LFC-LGBM + HFC-LSTM), and (LFC-LSTM + HFC-LGBM). The best-performing model (LFC-LGBM + HFC-LGBM) achieves a MAE of 4.9420%, a RMSE of 7.1047%, and a correlation index (R) of 0.9734 for 15-min ahead timesteps. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.