{"id":"W3161067985","doi":"10.14288/1.0397471","title":"Short term electric load forecasting for British Columbia, Canada: an exploration of the use of numerical weather prediction data as a predictor in an artificial neural network","year":2021,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial neural network; Term (time); Meteorology; Numerical weather prediction; Weather forecasting; Computer science; Climatology; Environmental science; Artificial intelligence; Machine learning; Geography; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008017546,0.0009908604,0.0004570246,0.0005039491,0.001454681,0.001348414,0.001192497,0.0005442008,0.001433175],"category_scores_gemma":[0.003046421,0.0003231782,0.0004630955,0.001265112,0.0005093092,0.0005967788,0.0004968364,0.0009507042,0.0002737603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01465474,"about_ca_system_score_gemma":0.01200817,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9891872,"about_ca_topic_score_gemma":0.9879662,"domain_scores_codex":[0.9995885,0.00006609043,0.00001920625,0.0001139859,0.0001443893,0.00006771795],"domain_scores_gemma":[0.998517,0.0004337943,0.00006091721,0.00008996705,0.0007954749,0.000102834],"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":[0.001348728,0.001002909,0.1587426,0.0003957459,0.0004380663,0.0005006096,0.0007436105,0.6636192,0.007985453,0.001351839,0.01608879,0.1477825],"study_design_scores_gemma":[0.0001281838,0.0001457852,0.1175291,0.00005899133,0.0001363821,0.00003148903,0.000820675,0.8744444,0.002809023,0.0003023888,0.003514475,0.00007912899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988371,0.0004329189,0.002073108,0.0005754979,0.0000362315,0.00008259297,0.00260113,0.0002545358,0.005572884],"genre_scores_gemma":[0.9880403,0.0003340803,0.003490377,0.00009848922,0.000007367218,0.00004673888,0.004443361,0.00003224178,0.003507051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01465474,"threshold_uncertainty_score":0.1063281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04610923476527818,"score_gpt":0.1931147674938912,"score_spread":0.147005532728613,"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."}}