Photovoltaic Power Forecasting Based on Variational Mode Decomposition and Long Short-Term Memory Neural Network

Abstract

The accurate forecasting of photovoltaic (PV) power is vital for grid stability. This paper presents a hybrid forecasting model that combines Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM). The model uses VMD to decompose the PV power into modal components and residuals. These components are combined with meteorological variables and their first-order differences, and feature extraction techniques are used to generate multiple sets of feature vectors. These vectors are utilized as inputs for LSTM sub-models, which predict the modal components and residuals. Finally, the aggregation of prediction results is used to achieve the PV power prediction. Validated on Australia’s 1.8 MW Yulara PV plant, the model surpasses 13 benchmark models, achieving an MAE of 63.480 kW, RMSE of 81.520 kW, and R2 of 92.3%. Additionally, the results of a paired t-test showed that the mean differences in the MAE and RMSE were negative, and the 95% confidence intervals for the difference did not include zero, indicating statistical significance. To further evaluate the model’s robustness, white noise with varying levels of signal-to-noise ratios was introduced to the photovoltaic power and global radiation signals. The results showed that the model exhibited higher prediction accuracy and better noise tolerance compared to other models.