Wind power probabilistic forecasting based on combined decomposition and deep learning quantile regression

With the expansion of scale of the grid-connected wind power, wind power forecasting plays an increasing important role in ensuring the security and steady operation and instructing the dispatch of power systems. In consideration of the randomness and intermittency of wind power, the probabilistic forecasting is required in quantifying the uncertainty of wind power. This study proposes a probabilistic wind power prediction method that combines variational modal decomposition (VMD), singular spectrum analysis (SSA), quantile regression (QR), convolutional neural network (CNN) and bidirectional gated neural network (BGRU). Firstly, a combination decomposition method VMDS combining VMD and SSA is proposed to decompose wind power sequence to reduce the complexity of the sequence. Next, a feature extractor based on CNN and BGRU (CBG) is used to extract complex dynamic features of NWP data and high-frequency components. Then, the QR is performed by the BGRU based on the high-order features to obtain the predicted values for different quantiles. Finally, the kernel density estimation (KDE) is employed to estimate the probability density curve of wind power. The proposed model can achieve reliable probabilistic prediction while achieving accurate deterministic prediction. According to comparisons with related prediction models, the effectiveness of the proposed method is verified with the example test using datasets from the wind farm in China.