Probabilistic imputation of missing offshore wind speed based on conditional diffusion models

Abstract

The growing demand for reliable renewable energy underscores the pivotal role of offshore wind power, renowned for its consistent and robust wind speeds. However, harsh weather conditions often lead to sensor failures and communication disruptions at sea, resulting in missing wind speed data. Such data gaps significantly hinder the accuracy of wind power forecasting, power curve modeling, and energy assessments of wind turbines—critical tasks for efficient operation and maintenance. To address these challenges, this work introduces an innovative imputation framework for missing wind speed data in offshore wind farms, leveraging a conditional diffusion model. By framing the imputation as a conditional generation problem, the approach employs multi-head attention mechanisms and graph convolutional networks to effectively capture spatiotemporal correlations and generate context-aware information. A denoising network then transforms random noise into accurate estimates for the missing values, while adaptive bandwidth kernel density estimation (ABKDE) is used to estimate the distribution of missing wind speed, providing probabilistic intervals for imputation. Extensive experiments on real-world datasets across a variety of missing data scenarios demonstrate that the proposed method outperforms existing benchmarks. Not only does it yield precise deterministic imputation results, but it also quantifies uncertainty by providing probabilistic intervals for the imputed values. This significantly enhances the reliability and accuracy of wind speed imputation. Experiments have demonstrated that the proposed method is particularly effective in handling complex missing data patterns, such as those caused by long-term sensor failures or extreme weather events, and it can improve the performance of downstream prediction tasks. This work provides a novel and robust solution for missing data imputation in offshore wind farms, offering more reliable and interpretable results for downstream tasks, including risk management and wind power optimization.