{"id":"W2915629347","doi":"10.1109/tpwrs.2018.2872822","title":"Very Short-Term Wind Power Prediction Interval Framework via Bi-Level Optimization and Novel Convex Cost Function","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Benchmark (surveying); Prediction interval; Term (time); Electric power system; Interval (graph theory); Computer science; Minification; Wind power; Convex optimization; Differentiable function; Hyperparameter; Function (biology); Operator (biology); Power (physics); Engineering; Mathematics; Regular polygon; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0001755728,0.0003262818,0.0003204167,0.0002613498,0.0001490398,0.0001355493,0.0001068826,0.0003564803,0.0002587767],"category_scores_gemma":[0.000002922857,0.0003393986,0.0001058178,0.0002684109,0.00003801495,0.0004854908,0.000002068435,0.000466904,0.00006814337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001443197,"about_ca_system_score_gemma":0.00001716663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002642691,"about_ca_topic_score_gemma":0.000007981329,"domain_scores_codex":[0.9985138,0.00003645814,0.0004626857,0.0003742346,0.0002826053,0.0003302095],"domain_scores_gemma":[0.9992821,0.0001010331,0.00005823463,0.0003395264,0.00007848765,0.0001405853],"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":[0.0000695545,0.00007492299,0.000593573,0.000111214,0.0001867945,0.000001995315,0.0004532982,0.9920651,0.005591446,0.00008256797,0.00007779793,0.0006916759],"study_design_scores_gemma":[0.001242958,0.0006158069,0.002392306,0.001226875,0.0001468169,0.0001617065,0.0004012304,0.9868848,0.003325852,0.00001030725,0.002784692,0.0008066525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07993054,0.0001537412,0.9035509,0.000009624719,0.0138375,0.0004632461,0.0001493471,0.0004293679,0.001475725],"genre_scores_gemma":[0.9987665,0.00003801961,0.0005197052,0.00003500112,0.00006633528,0.00004615419,0.00002778158,0.00009085612,0.000409587],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.918836,"threshold_uncertainty_score":0.9999058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01623981703473089,"score_gpt":0.2075407497859328,"score_spread":0.1913009327512019,"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."}}