Spatio-temporal coordination-aware imputation of wind farm power data: An improved GARIN

Turbine power time series exhibit spatiotemporal coordination that is challenging to model jointly. We present an improved Graph Attention Recurrent Imputation Network (GARIN): a farm-level proximity graph captures spatial correlations; graph attention embedded within a GRU adapts dependency modeling at each time step; and a bidirectional design leverages past–future context for robustness to long gaps.

On real SCADA datasets, GARIN consistently outperforms strong baselines; at an 80% missing rate, errors drop by 23.9% (RMSE) and by 8.5%/8.4% (MAE/MRE) relative to the best baseline. Ablation studies confirm the contributions of graph attention and bidirectionality. GARIN enables accurate, robust imputation for coordination-rich wind farms.</p>