Short-term wind power prediction method based on data cleansing and online learning

With the continuous growth of global energy demands and the intensification of environmental pollution issues, the development and utilization of renewable energy have become particularly crucial. Wind energy, as a clean and renewable energy source, has received extensive attention in its development and utilization. However, the volatility and intermittency of wind energy pose challenges to the stable operation of the power grid. Therefore, accurate wind power prediction is essential for ensuring the safe grid integration of wind energy. This paper proposes a wind power prediction method based on data cleansing and online learning to improve the precision. The experiments verify the proposed method reduces the RSM error by 0.08.