{"id":"W3004665554","doi":"10.1016/j.energy.2020.117081","title":"Wind power forecasting using attention-based gated recurrent unit network","year":2020,"lang":"en","type":"article","venue":"Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":321,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Wind power forecasting; Feature selection; Computer science; Wind power; Selection (genetic algorithm); Task (project management); Artificial intelligence; Machine learning; Model selection; Electric power system; Feature (linguistics); Power (physics); Sequence (biology); Data mining; Engineering; Systems engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007949199,0.0002204435,0.0002042381,0.00005052967,0.0001299062,0.00004709983,0.0001388517,0.0000933218,0.0002511526],"category_scores_gemma":[0.00003181362,0.0002351753,0.00009559273,0.0005026288,0.00002122682,0.0001204765,0.00003370904,0.0001717625,0.00001109617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003770983,"about_ca_system_score_gemma":0.00002688075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003571821,"about_ca_topic_score_gemma":0.00002336811,"domain_scores_codex":[0.9988672,0.00003636269,0.000286468,0.0002161232,0.0001586895,0.0004351663],"domain_scores_gemma":[0.9995161,0.00006483581,0.00005465957,0.000140889,0.00004119992,0.000182321],"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":[0.00001133688,0.000006794332,0.0005928281,0.00002201701,0.00003492502,0.00002059924,0.00006529467,0.9913771,0.001450831,0.0005544125,0.001002492,0.004861385],"study_design_scores_gemma":[0.0002397845,0.00004710533,0.00008314735,0.0001474156,0.00001895853,0.000005086082,0.00001692908,0.9284602,0.001301085,0.00004971142,0.06934153,0.0002890625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8496078,0.002090591,0.11695,0.0002527439,0.003058895,0.0001079888,0.00002104778,0.001470365,0.02644055],"genre_scores_gemma":[0.9962878,0.000007981284,0.00248716,0.0003860435,0.0006505918,0.000001922362,0.00006055458,0.00007276477,0.00004514758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1466801,"threshold_uncertainty_score":0.959017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03768735253833277,"score_gpt":0.2186283451525827,"score_spread":0.1809409926142499,"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."}}