{"id":"W4205105140","doi":"10.32920/ryerson.14668095","title":"Wind energy forecasts in calculation of expected energy not served","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Markov chain; Wind power; Energy (signal processing); Autoregressive–moving-average model; Computer science; Moving average; Benchmark (surveying); Power (physics); Mathematical optimization; Autoregressive model; Econometrics; Engineering; Mathematics; Statistics","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":[0.001448411,0.0003726846,0.0005337032,0.0005276308,0.0001876188,0.001032372,0.000517236,0.0006467298,0.000793942],"category_scores_gemma":[0.01144419,0.0004461803,0.0003749857,0.0007529638,0.0003454005,0.001245243,0.0004524873,0.0008806042,0.0002593525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006682512,"about_ca_system_score_gemma":0.001344144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01219267,"about_ca_topic_score_gemma":0.009130705,"domain_scores_codex":[0.9992065,0.000220821,0.00004794175,0.0001190484,0.0003565576,0.00004930525],"domain_scores_gemma":[0.996747,0.002162971,0.0003072279,0.0002779077,0.0004501132,0.00005471492],"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":[0.00001797297,0.000008101486,0.002279164,0.000012922,0.00001182022,0.00002741665,0.00001696624,0.9788867,0.0003585265,0.005357874,0.000296791,0.01272569],"study_design_scores_gemma":[0.000002396808,0.000004260582,0.0005000304,0.000004867287,0.000001438715,0.000006468507,0.000004613576,0.9966481,0.0003185525,0.002338402,0.0001672763,0.000003547395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1770255,0.0002931674,0.8167211,0.0004195956,0.0001138912,0.00003338344,0.0004078689,0.0006303861,0.004355092],"genre_scores_gemma":[0.9360294,0.0003208722,0.06140283,0.00004482931,0.0000314589,0.00003966248,0.000517964,0.0001143041,0.00149867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01219267,"threshold_uncertainty_score":0.02424341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01933944317152492,"score_gpt":0.2137611192579834,"score_spread":0.1944216760864584,"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."}}