DWT-DTCNA Ultra-short-term Wind Power Prediction Considering Wind Power Timing Characteristics

Ultra-short-term wind power forecasting is of great significance for the formulation of power system production scheduling plans, but wind power output is largely affected by weather factors with the characteristics of strong randomness, volatility, and uncontrollability. At the same time, the impact of wind power uncertainty on the wind power time series relationship poses a challenge to the accuracy of wind power prediction. Fully extracting the wind power time series characteristics has become an important way to solve this problem. Aiming at the this problem, an ultra-short-term wind power prediction based on the DWT-DDQN-TCN-Attention (DWT-DTCNA) network is proposed, consisting of the Discrete Wavelet Transformation (DWT), the Double Depth Q Network (DDQN), and the Temporal Convolutional Network (TCN) and the Attention Mechanism. First, the DWT is used to decompose the wind power data series into the wind power data sets of different frequencies. The autocorrelation function analysis is performed on the wind power data sets of different frequencies. The wind power training subset is extracted with high autocorrelation as the input of the prediction model. Secondly, according to the different frequency wind power datasets obtained after the DWT decomposition, the corresponding TCN-Attention wind power ultra-short-term prediction model is trained, and the wind power timing relationship is deeply excavated. In order to reduce the influence of the parameters of the deep learning model on the prediction accuracy, the parameters of the prediction model are optimized by the DDQN algorithm. Finally, the ultra-short-term wind power prediction results of different frequencies are reconstructed by the DWT to obtain the wind power sequence of the forecast day. Taking the measured data of a wind farm in northwest China as an example, the simulation and analysis results show that the proposed method fully explores the timing relationship of wind power sequence, optimizes the internal parameters of the model, and effectively improves the ultra-short-term wind power prediction accuracy. © 2023 Power System Technology Press. All rights reserved.