{"id":"W4392943060","doi":"10.1109/icetsis61505.2024.10459633","title":"A Proposed Hybrid Deep Learning Model for Wind Power Forecasting","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Wind power forecasting; Wind power; Computer science; Deep learning; Power (physics); Artificial intelligence; Meteorology; Electric power system; Engineering; Electrical engineering; Geography","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":[0.0002252288,0.0004535976,0.0004493532,0.0002673408,0.0002554547,0.0005072498,0.0009149187,0.0007779403,0.002659323],"category_scores_gemma":[0.0003237897,0.0002361716,0.0004609116,0.0003835917,0.0001772905,0.0008733403,0.0004391427,0.0007460304,0.0005767324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003793768,"about_ca_system_score_gemma":0.0005694436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007595802,"about_ca_topic_score_gemma":0.009174173,"domain_scores_codex":[0.9999113,0.0000128417,0.000006639189,0.00002789017,0.00002583733,0.00001538863],"domain_scores_gemma":[0.999921,0.00002061034,0.000007819141,0.000006993442,0.00003802147,0.000005538381],"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":[0.00008202618,0.00005989991,0.0008373244,0.0000782865,0.00006087999,0.0001035498,0.0000342932,0.8681376,0.006906616,0.006650458,0.002225716,0.1148233],"study_design_scores_gemma":[0.000002029976,0.000007661197,0.0000636394,0.000001925961,0.000003035203,0.000006591692,0.00000101556,0.9988015,0.0002809638,0.0005453566,0.0002841819,0.00000216721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0421343,0.000742815,0.9470252,0.0003824776,0.0001924759,0.00004015072,0.0003776572,0.001166436,0.007938547],"genre_scores_gemma":[0.8782952,0.000620269,0.103075,0.0002196591,0.00008671077,0.0001744226,0.0006289885,0.00006347696,0.01683635],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007595802,"threshold_uncertainty_score":0.01510316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01895000285623004,"score_gpt":0.2159524076104047,"score_spread":0.1970024047541746,"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."}}