Wind power forecasting based on SCINet, reversible instance normalization, and knowledge distillation

Wind energy plays a crucial role as a clean energy source in the electricity system. The unpredictability of wind power makes it more challenging to put into use in comparison to thermal power generation. Accurate wind power prediction algorithms are of great importance for allocation and deployment of wind power. In this paper, a novel time-series forecasting model, SCINet, is used for short-term wind power forecasting and achieves high forecasting accuracy. Furthermore, the addition of reversible instance normalization (RevIN) to SCINet effectively alleviates the shift problem that arises in time series forecasting tasks. This enhancement further improves the model's forecasting ability. Finally, this paper uses knowledge distillation to get a small model that could speed up the computing and save memory resources. The source code is available at https://github.com/raspnew/WPF.git.