{"id":"W3003350829","doi":"10.1109/pesgm40551.2019.8973554","title":"Direct Interval Forecast of Uncertain Wind Power Based on Recurrent Neural Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Prediction interval; Wind power; Recurrent neural network; Computer science; Artificial neural network; Interval (graph theory); Nonparametric statistics; Mathematical optimization; Electric power system; Upper and lower bounds; Power (physics); Artificial intelligence; Machine learning; Statistics; Mathematics; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001124154,0.0001637838,0.0002072663,0.00007534779,0.00001499853,0.00001488042,0.0001237214,0.00006861544,0.000619062],"category_scores_gemma":[0.00001143046,0.000136204,0.0001064253,0.0001433876,0.00001475964,0.00006103561,0.00002061517,0.0001579648,0.00001753428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003182934,"about_ca_system_score_gemma":0.000005712556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001480521,"about_ca_topic_score_gemma":0.00001516654,"domain_scores_codex":[0.9992275,0.00002150128,0.0002181672,0.0001500737,0.0001287679,0.0002539494],"domain_scores_gemma":[0.9995616,0.0001118332,0.00003141991,0.0002135037,0.0000217213,0.00005991904],"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":[0.00002997829,0.00002048029,0.0028981,0.00002922134,0.00001724698,0.000002193686,0.00005857963,0.9866887,0.0001532309,0.0001562707,0.0008021109,0.009143891],"study_design_scores_gemma":[0.0002976536,0.0002164853,0.0006568024,0.0001309015,0.000005412745,0.000001265476,0.00001852525,0.9944652,0.001617663,0.000003621948,0.002421915,0.0001645605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7675282,0.0001188017,0.002977782,0.00002810343,0.002289987,0.000141342,0.000008714385,0.0002298306,0.2266773],"genre_scores_gemma":[0.999267,0.000002981883,0.0002108075,0.00007503966,0.00006591798,0.000002218869,0.00001539963,0.00003192753,0.0003286901],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2317389,"threshold_uncertainty_score":0.6778295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01127307820257624,"score_gpt":0.211401112481578,"score_spread":0.2001280342790018,"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."}}