{"id":"W2585140149","doi":"10.1109/icmla.2016.0135","title":"An Empirical Study on Machine Learning Models for Wind Power Predictions","year":2016,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Wind power; Computer science; Machine learning; Artificial intelligence; Empirical research; Support vector machine; Deep learning; Renewable energy; Power (physics); Wind speed; Wind power forecasting; Predictive modelling; Empirical modelling; Data modeling; Electric power system; Simulation; Engineering; Meteorology; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01190216,0.001002153,0.00065302,0.001108014,0.0004777675,0.001486818,0.0009449828,0.0009262017,0.001990066],"category_scores_gemma":[0.06134837,0.0004063571,0.0007560418,0.00227889,0.0009886798,0.00373451,0.0006770468,0.002208383,0.0004947603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001087183,"about_ca_system_score_gemma":0.0007950254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009024585,"about_ca_topic_score_gemma":0.007030695,"domain_scores_codex":[0.9959767,0.002010981,0.0002762465,0.0004763671,0.001123776,0.0001359138],"domain_scores_gemma":[0.9017991,0.08736265,0.002247203,0.003725999,0.00459879,0.0002662711],"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":[0.0005138037,0.0008281707,0.2977041,0.0009083566,0.0005467167,0.0003338166,0.0006153935,0.4346528,0.001293222,0.02857118,0.0121106,0.2219218],"study_design_scores_gemma":[0.00003424591,0.0002936439,0.05660241,0.0004668354,0.00008641354,0.0003115907,0.0004681906,0.9139249,0.001829164,0.01477482,0.01115826,0.00004960103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8055404,0.01681233,0.14787,0.006438474,0.0002579002,0.0001538082,0.003127991,0.0005334367,0.01926563],"genre_scores_gemma":[0.9769148,0.002822961,0.01617639,0.0002516169,0.00009703397,0.0000609502,0.002362207,0.00006714588,0.001246923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01190216,"threshold_uncertainty_score":0.06294537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03446853891311322,"score_gpt":0.2730317427416469,"score_spread":0.2385632038285337,"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."}}