Mechanism Modeling and Uncertainty Correction for Wind Power Forecasting Under Stagnant Weather Conditions: A Case Study in Xinjiang

Abstract

To address the substantial increase in wind power forecasting errors under stable weather conditions, this paper examines a typical wind farm in Xinjiang and systematically analyzes the uncertainty mechanism through which the power curve nonlinearly amplifies wind speed forecasting errors. On this basis, a multimodule collaborative modeling method that integrates mechanistic understanding with data-driven approaches is proposed. First, a state-partitioning method characterizes nonstationary features under different operating conditions, and a wind speed correction module dynamically corrects the wind speed provided by numerical weather prediction (NWP), thereby reducing input errors at their source. Second, an error-state transition model based on Markov chains characterizes the temporal dependence and state-evolution characteristics of forecasting errors. Furthermore, a deep learning model produces deterministic forecasts, while error-distribution modeling and a dual-model collaborative mechanism construct probabilistic prediction intervals. Experimental results demonstrate that the proposed method significantly outperforms traditional approaches and mainstream deep learning models in both single-step and multistep forecasting tasks. It also achieves higher interval coverage and a better ability to fit the underlying distribution during probabilistic forecasting.