Frequency-Domain Decomposition and Deep Learning Based Solar PV Power Ultra-Short-Term Forecasting Model

Ultra-short-term photovoltaic (PV) power forecasting can support the real-time dispatching of power grid. However, PV power has great fluctuations due to various meteorological factors, which increases energy prices and cause difficulties in managing the grid. This paper proposes an ultra-short-term PV power forecasting model based on optimal frequency-domain decomposition(FDD) and deep learning. First, the optimal frequency demarcation points for decomposition components are obtained through frequency domain analysis. Then the PV power is decomposed into the low-frequency and high-frequency components, which supports the rationality of decomposition results and solves the problem that the current decomposition model only uses the direct decomposition method and the decomposition components are not physical. Then convolutional neural network(CNN) is used to forecast the low-frequency and high-frequency components, and final forecasting result is obtained by addition reconstruction. Based on actual PV data in heavy rain days, the MAPE of the proposed forecasting model is decreased by 52.97%, 64.07% and 31.21%, compared with discrete wavelet transform, variational mode decomposition and direct prediction models. In addition, compared with Recurrent neural network and Long-short-term memory model, the MAPE of CNN forecasting model is decreased by 23.64% and 46.22%, and the training efficiency of CNN forecasting model is improved by 85.63% and 87.68%. The results fully show that the proposed model in this paper can improve both the forecasting accuracy and time efficiency significantly.