Wind Power Forecasting in the presence of data scarcity: A very short-term conditional probabilistic modeling framework

Abstract

The uncertainty of wind power (WP) poses a significant challenge to power systems with a high percentage of WP. Accurate WP forecasting is an important approach to mitigate this issue. With the increasing demand for electricity, some wind farms (WFs) have been built or expanded. Such WFs lack abundant historical data for establishing forecasting models, i.e., data scarcity. This paper proposes a novel method for very short-term probabilistic forecasting based on transfer learning (TL) and conditional screening for WP. Utilizing numerical weather prediction as predictive features to address deterministic forecast data scarcity. Considering the data orientation, combining bidirectional long short-term memory with attention mechanisms to enhance prediction accuracy. Based on TL and conditional screening, it achieves data completion and singular value filtering to enhance the comprehensive performance of probabilistic forecasting errors. Using real world data, the proposed WP forecasting model reduces MAPE by at least 0.87% and RMSE by at least 0.6483 compared to the benchmark at different forecast steps. The comprehensive performance of probabilistic forecasting improves by at least 1.9134 at different forecast steps and confidence levels. The results indicate that the proposed forecasting model for data scarcity WFs is feasible, providing new insights for data scarcity WF forecasting.