Photovoltaic output prediction based on VMD disturbance feature extraction and WaveNet

Abstract

Traditional photovoltaic (PV) forecasting methods often overlook the impact of the correlation between different power fluctuations and weather factors on short-term forecasting accuracy. To address this, this paper proposes a PV output forecasting method based on Variational Mode Decomposition (VMD) disturbance feature extraction and the WaveNet model. First, to extract different feature variations of the output and enhance the model’s ability to capture PV power fluctuation details, VMD is used to decompose the PV output time series, obtaining IMFs modes representing output disturbances and quasi-clear sky IMF modes. Then, to reveal power changes, especially the underlying patterns of disturbances and their relationship with weather factors, K-means clustering is applied to the IMF modes representing output disturbances, clustering the disturbance IMFs into different power change feature clusters. This is combined with Spearman correlation analysis of weather factors and the construction of an experimental dataset. Finally, to enhance the model’s learning ability and improve short-term output forecasting accuracy, the WaveNet model is employed during the forecasting phase. Separate WaveNet models are constructed and trained with the corresponding datasets, and the total PV output forecast is obtained by superimposing the predictions of different IMF modes. Experimental results are compared with traditional methods, demonstrating a significant improvement in forecasting accuracy, with a Mean Absolute Percentage Error (MAPE) error of 6.94%, highlighting the effectiveness of our method and providing strong technical support for the refined management and intelligent forecasting of PV energy.