{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008889334,0.0003383923,0.0005221747,0.0002919472,0.00001658635,0.00003848396,0.0002004144,0.0004732159,0.000227226],"category_scores_gemma":[0.00002430781,0.0003689157,0.0001735863,0.0003039018,0.00001868389,0.00009228305,0.0002377062,0.0002710191,5.453029e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001117057,"about_ca_system_score_gemma":0.00006239636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00396357,"about_ca_topic_score_gemma":0.00715902,"domain_scores_codex":[0.9984151,0.00005436504,0.0006372124,0.0003554748,0.0002313639,0.000306447],"domain_scores_gemma":[0.9992471,0.00006692646,0.0001042189,0.0004145837,0.00009303916,0.00007411621],"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":[0.00001184612,0.00003329938,0.0005958596,0.0002173366,0.0001037454,0.00003208154,0.0006612196,0.9790449,0.006333848,0.001774595,0.0001343829,0.01105694],"study_design_scores_gemma":[0.0005761946,0.00002103914,0.003822772,0.0008647034,0.00003124586,0.00000965453,0.00009381122,0.8226336,0.1693812,0.000247282,0.001558368,0.0007600932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9445293,0.0009620101,0.0291573,0.0000183659,0.001262003,0.00005653027,0.00001551667,0.000293981,0.02370499],"genre_scores_gemma":[0.9965801,0.0001438518,0.001840102,0.0000314645,0.0001577857,0.00002005238,0.0004866402,0.00007166022,0.0006683577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1630474,"threshold_uncertainty_score":0.9998763,"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."}}