Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model

The stability operation and real-time control of the integrated energy system with distributed energy resources determines the higher and higher requirements for the accuracy of solar photovoltaic (PV) output power pre-diction. This paper proposes an accurate PV power prediction interval approach based on frequency-domain decomposition and hybrid deep learning (DL) model. In the proposed approach, ensemble empirical mode decomposition (EEMD) is firstly used to decompose and reconstruct the original PV energy time-series data into high and low-frequency sub-series followed by the statistical feature extraction process. Furthermore, an improved long-short-term-memory network (LSTM) model with the designed hyperparameters based on Bayesian optimization (BO) is proposed to predict the sub-series with the different minute-hour-day intervals. Moreover, support vector regression (SVR) is utilized to analyze the initial time node and reduce the fluctuation error of the prediction value near zero. Finally, a comparative study with SVR, KNN, BPNN, GRU, Stacked-LSTM, LSTM, LSTM-SVR, and LSTM-SVR-BO models is constructed by using an actual dataset collected from Arizona, US. The simulation results on the datasets show the proposed prediction model outperforms the other 7 models for PV power forecasting in 1 day, 7 days, and 14 days ahead prediction with the different minute-hour-day intervals. Especially, in the seven days ahead prediction case, the proposed model's average RMSE and Abs-DEV values are as low as 4.157 and 0.116, where the prediction accuracy and prediction stability are improved by about 15% on average compared to the other prediction models.