{"id":"W3143924650","doi":"10.3390/app11073048","title":"Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Program for Changjiang Scholars and Innovative Research Team in University; National Natural Science Foundation of China","keywords":"Quantile; Wind power; Outlier; Quantile regression; Turbine; Probabilistic logic; SCADA; Logistic regression; Logistic distribution; Wind power forecasting; Computer science; Statistics; Power (physics); Econometrics; Engineering; Mathematics; Electric power system","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":[0.001429554,0.0008408805,0.0007244598,0.0008792907,0.0002959326,0.001003578,0.001251397,0.0009014235,0.001464941],"category_scores_gemma":[0.004694634,0.0004180727,0.001213317,0.001404361,0.0004442211,0.001561209,0.0007165424,0.001196092,0.0005291717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006751064,"about_ca_system_score_gemma":0.0005626662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007352517,"about_ca_topic_score_gemma":0.002655834,"domain_scores_codex":[0.9992629,0.0002544839,0.00004410642,0.0001805208,0.0001735588,0.00008446063],"domain_scores_gemma":[0.9987802,0.0006408354,0.0001873209,0.0001016548,0.0002629393,0.00002715689],"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":[0.0000333745,0.00001547404,0.002437082,0.00004787986,0.00002470225,0.0000951601,0.00005612267,0.9575887,0.001080493,0.004833187,0.0004324273,0.0333554],"study_design_scores_gemma":[0.000001023191,0.000005403679,0.0002743643,0.000002834328,0.000002443651,0.00001475012,0.000004308632,0.9984152,0.0001737155,0.0008974833,0.0002040108,0.000004412685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02032464,0.0002237449,0.9778831,0.0001137685,0.0000148153,0.00002805887,0.00008871339,0.000372285,0.0009508118],"genre_scores_gemma":[0.9069495,0.0007911423,0.08837172,0.00006049408,0.00005026997,0.0001446431,0.0004207608,0.0002082285,0.003003255],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007352517,"threshold_uncertainty_score":0.01461947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03502635051081653,"score_gpt":0.2360327990609904,"score_spread":0.2010064485501739,"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."}}