{"id":"W2149906128","doi":"10.1109/tpwrs.2014.2299801","title":"A Hybrid Intelligent Model for Deterministic and Quantile Regression Approach for Probabilistic Wind Power Forecasting","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":203,"is_retracted":false,"has_abstract":true,"ca_institutions":"Teshmont (Canada)","funders":"National Renewable Energy Laboratory","keywords":"Wind power; Wind power forecasting; Probabilistic forecasting; Probabilistic logic; Support vector machine; Electric power system; Wind speed; Computer science; Particle swarm optimization; Quantile regression; Engineering; Mathematical optimization; Artificial intelligence; Machine learning; Power (physics); Meteorology; Mathematics","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":[0.0006914973,0.0004893391,0.000696581,0.0003732291,0.0002958299,0.0007378563,0.001239905,0.0008292438,0.001459884],"category_scores_gemma":[0.001373388,0.0003132453,0.0006561724,0.0006125123,0.0002393491,0.0007104033,0.0004125088,0.0007540376,0.0002890382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005594552,"about_ca_system_score_gemma":0.0005393264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01004818,"about_ca_topic_score_gemma":0.006110869,"domain_scores_codex":[0.9996785,0.00009405216,0.00001969516,0.00008596948,0.00007979233,0.0000420266],"domain_scores_gemma":[0.9997097,0.000151372,0.0000411738,0.00001511038,0.00007397677,0.000008716491],"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":[0.00002156206,0.00001623246,0.0004429517,0.00001656424,0.00001861792,0.00002678211,0.00001581696,0.9774224,0.0005975446,0.002984569,0.0002889801,0.018148],"study_design_scores_gemma":[8.978109e-7,0.000004103946,0.0000545526,7.709792e-7,0.000001919796,0.000003076861,7.884274e-7,0.999511,0.000051483,0.0002972964,0.00007279193,0.000001353464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02217784,0.0003782737,0.9744586,0.0001805716,0.00003797367,0.00002745945,0.00007559791,0.0003478033,0.002315888],"genre_scores_gemma":[0.8992237,0.0004292701,0.09613202,0.00007987051,0.00005161649,0.0001355082,0.0001786383,0.00004739425,0.003721905],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01004818,"threshold_uncertainty_score":0.01997942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03460675173194757,"score_gpt":0.23671738082361,"score_spread":0.2021106290916624,"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."}}