2026-10-10T06:00:41Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33968583 2026-09-22T18:31:33Z category_15 category_45 category_46 category_734 category_931 category_811 category_106 category_122 portal_87 item_type_3 month_year_09_2026

Spatio-temporal coordination-aware imputation of wind farm power data: An improved GARIN Liulin Yang (1584655) Cheng Zheng (227957) Shujia Zeng (25091415) Yiming Cai (13042518) Zhi Wei (258408) Neuroscience Sociology Immunology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Science Policy Mental Health Deep learning graph attention networks (GAT) spatiotemporal coordination time series wind farm power imputation <p>Wind-farm SCADA data frequently contain large gaps caused by severe weather and sensor faults, undermining performance assessment and secure operation. 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> 2026-09-22T18:31:33Z Dataset Dataset 10.6084/m9.figshare.33968583.v1 https://figshare.com/articles/dataset/Spatio-temporal_coordination-aware_imputation_of_wind_farm_power_data_An_improved_GARIN/33968583 CC BY 4.0