{"id":"W3091288105","doi":"10.1109/aparm49247.2020.9209346","title":"Wind Turbine Power Output Estimation with Probabilistic Power Curves","year":2020,"lang":"en","type":"article","venue":"2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM)","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Weibull distribution; Wind power; Probabilistic logic; Turbine; Probability density function; Monte Carlo method; Wind speed; Power (physics); Probability distribution; Computer science; Statistical model; Power optimizer; Control theory (sociology); Mathematics; Engineering; Meteorology; Statistics; Artificial intelligence; Electrical engineering; Maximum power point tracking; Physics; Aerospace engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002834222,0.0003880624,0.0003606113,0.00006112473,0.0001227786,0.00009010368,0.0002965314,0.0001064684,0.00007279393],"category_scores_gemma":[0.0005334636,0.0003162773,0.00008301658,0.0002376517,0.0001243546,0.0003872786,0.00008094816,0.0004911274,0.00004651953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002584688,"about_ca_system_score_gemma":0.00007776502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007143859,"about_ca_topic_score_gemma":0.000002391427,"domain_scores_codex":[0.9975826,0.00004158249,0.0005108258,0.0007136263,0.0006567372,0.0004946034],"domain_scores_gemma":[0.9986616,0.00007854847,0.00007154945,0.0003050042,0.0005464563,0.0003368548],"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":[0.0005188481,0.00007860255,0.00006666003,0.0002037178,0.00006793291,0.000019272,0.000525361,0.9937827,0.0007600508,0.0006811463,0.002365507,0.0009301864],"study_design_scores_gemma":[0.001136102,0.0003897898,0.0001536053,0.0005881982,0.0000158026,0.0000191107,0.0002978663,0.9824215,0.0006038979,0.0008040935,0.0130605,0.0005095564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5080398,0.001319208,0.3177477,0.06515335,0.003375421,0.003033736,0.0003136896,0.001852462,0.09916466],"genre_scores_gemma":[0.991044,0.0006632568,0.006941388,0.0005527618,0.00009700855,0.00008207786,0.00009559503,0.00005667603,0.0004672727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4830042,"threshold_uncertainty_score":0.999929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00851339408301658,"score_gpt":0.2153598198400692,"score_spread":0.2068464257570527,"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."}}