{"id":"W7106329570","doi":"10.1016/j.epsr.2025.112534","title":"Hybrid model for short-term wind power prediction by the SAO and a parallel architecture of LSTM and GRU","year":2025,"lang":"en","type":"article","venue":"Electric Power Systems Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Major Science and Technology Projects in Yunnan Province; Yunnan Provincial Department of Education","keywords":"Adaptability; Key (lock); Mean squared error; Grid; Power (physics); Wind power; Electric power system; Feature (linguistics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003707517,0.0007557113,0.0006913827,0.0003173711,0.0004260399,0.0006058326,0.0009877608,0.0008212305,0.002942719],"category_scores_gemma":[0.0006212907,0.0003733316,0.000672201,0.0004662435,0.0002747353,0.001075533,0.0005071732,0.001264989,0.0008884023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003516422,"about_ca_system_score_gemma":0.0008194108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01229591,"about_ca_topic_score_gemma":0.01699585,"domain_scores_codex":[0.9998912,0.00001719644,0.000009329981,0.00003899716,0.00002194112,0.00002143493],"domain_scores_gemma":[0.9998072,0.00005015144,0.00001358141,0.00002588339,0.00008673601,0.00001648148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003471829,0.0001816218,0.002016379,0.0001407977,0.0002347119,0.0001255282,0.00007523272,0.7775038,0.01680094,0.003227714,0.004398925,0.1949472],"study_design_scores_gemma":[0.000005194418,0.0000216679,0.0002130476,0.000002377366,0.00001222055,0.000008794742,0.000003609365,0.9981239,0.000871738,0.0004735905,0.0002594136,0.000004348194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1346489,0.001655359,0.8503108,0.0005804443,0.001012897,0.00006915106,0.0006348857,0.004562189,0.006525381],"genre_scores_gemma":[0.8937263,0.0003902288,0.09793299,0.0001438689,0.0001769888,0.0001188529,0.0006789879,0.0001258499,0.00670598],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01229591,"threshold_uncertainty_score":0.02444869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01873480700136997,"score_gpt":0.2705821990131071,"score_spread":0.2518473920117371,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}