Diffusion‐based conditional wind power forecasting via channel attention

Abstract

Wind energy is one of the most significant renewable sources of energy while accurate and reliable wind power forecasting methods may greatly benefit power system planning and scheduling. Recently, many machine learning algorithms have shown significant advantages in how to extract temporal features for wind power forecasting. However, wind power curves in the time domain frequently display intermittent features and significant uncertainty, which is not favorable to precise and reliable forecasting. In this paper, the Diffusion and Channel Attention‐based Wind Power Forecasting (DC‐WPF) framework is proposed, which transforms wind power data into the frequency domain while applying advanced channel attention techniques to aid the model in capturing the frequency domain information and ultimately enhancing accuracy. With high‐accuracy results, DC‐WPF then proposes a diffusion‐based framework to transform the point forecasting results into probabilistic forecasts to capture the uncertainty. Finally, extensive experiments on six wind power plants show that our method can improve the point forecasting accuracy of wind power and provide advanced probabilistic forecasts at a multi‐time scale.