A Wind Power Forecasting Model Considering Peak Fluctuations

Wind power output sequences exhibit strong randomness and intermittency characteristics, traditional single forecasting models struggle to capture the internal features of sequences and are highly susceptible to interference from high-frequency noise. A short-term wind power forecasting method based on an improved Informer model is proposed. First, the temporal convolutional network (TCN) is introduced to enhance the model's ability to capture regional segment features along the temporal dimension, enhancing the model's receptive field to address wind power fluctuation under varying environmental conditions; Next, a discrete cosine transform (DCT) is employed for adaptive modeling of frequency dependencies between channels, converting the time series data into frequency-domain representations to extract its frequency features. These frequency domain features are then weighted using a channel attention mechanism to improve the model's ability to capture peak features and resist noise interference. Finally, the Informer generative decoder is used to output the power prediction results. The experimental results validate the effectiveness and practicality of the proposed model.