{"id":"W4387951217","doi":"10.1109/ccece58730.2023.10288790","title":"Wind Speed Forecasting using ARMA and Boosted Regression Tree Methods: A Case Study","year":2023,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Wind speed; Autoregressive–moving-average model; Wind power; Moving average; Computer science; Time horizon; Wind power forecasting; Regression analysis; Probabilistic forecasting; Meteorology; Term (time); Autoregressive model; Econometrics; Electric power system; Power (physics); Machine learning; Artificial intelligence; Engineering; Mathematical optimization; Mathematics; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.0005267277,0.0002037687,0.0002258114,0.0002147492,0.0001820435,0.00006564899,0.00006059635,0.00009421193,0.00002774092],"category_scores_gemma":[0.0000833189,0.0001694518,0.00003864423,0.0005444099,0.00001806962,0.0001636783,0.00009762511,0.0002227471,0.000004828868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002742224,"about_ca_system_score_gemma":0.00000767145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002212081,"about_ca_topic_score_gemma":0.0001349721,"domain_scores_codex":[0.9989696,0.00008284271,0.0002697721,0.0002263166,0.0001162602,0.0003351792],"domain_scores_gemma":[0.9994036,0.0002555869,0.00003544739,0.0001706622,0.00002375331,0.0001109647],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004349106,0.0001039778,0.02133679,0.0003344843,0.0003753424,0.02039049,0.01905775,0.2899594,0.07639193,0.00004119716,0.001067809,0.5708973],"study_design_scores_gemma":[0.0005867063,0.00005783969,0.0002669615,0.0001060161,0.00004271825,0.002183972,0.007380295,0.9857177,0.003143675,0.00003144236,0.0002291245,0.0002535446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929132,0.0001266452,0.001895066,0.000007727692,0.0004459586,0.0001829529,0.000001787825,0.0007310816,0.00369554],"genre_scores_gemma":[0.9835527,0.000006487692,0.01590352,0.00001135719,0.000118553,0.000001388666,0.000003395332,0.00005597999,0.0003466077],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6957583,"threshold_uncertainty_score":0.6910043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1065656413731207,"score_gpt":0.340752648237889,"score_spread":0.2341870068647683,"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."}}