{"id":"W2256360925","doi":"","title":"A Comparison of Aggregate and Multi-Region Load Forecasting Models in Saskatchewan","year":2012,"lang":"en","type":"dissertation","venue":"oURspace (University of Regina)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Aggregate (composite); Diversification (marketing strategy); Electrical load; Artificial neural network; Aggregate demand; Diversity (politics); Peak load; Electric power system; Computer science; Econometrics; Engineering; Power (physics); Economics; Artificial intelligence; Automotive engineering; Electrical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001153791,0.0002255638,0.0005260892,0.0002599766,0.00005383232,0.000006289151,0.0001596697,0.000292649,0.000001265018],"category_scores_gemma":[0.000009831457,0.0003067435,0.00008942839,0.0002123758,0.00005537818,0.0002716782,0.00002942823,0.0002991788,8.047623e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000973344,"about_ca_system_score_gemma":0.00005785812,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002145692,"about_ca_topic_score_gemma":0.02186363,"domain_scores_codex":[0.9993042,0.00002422646,0.00003887691,0.0001857712,0.0001785796,0.0002683643],"domain_scores_gemma":[0.9993571,0.00004320659,0.0002672808,0.0001651699,0.00008403673,0.00008323136],"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":[0.0006625166,0.0003027087,0.04639377,0.01181265,0.0007329865,0.0002086498,0.2268355,0.3826356,0.009080866,0.001237356,0.02539934,0.294698],"study_design_scores_gemma":[0.002023401,0.0001176014,0.003052032,0.004955986,0.0002694959,0.00001895954,0.1260913,0.839526,0.001850808,0.00002297371,0.02113893,0.0009325108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897737,0.003266504,0.004047768,0.0009887278,0.0002364015,0.0001277823,0.000008191731,0.00006365286,0.001487259],"genre_scores_gemma":[0.9831955,0.0002366017,0.0042648,6.047113e-7,0.00002168681,3.241885e-7,0.00006029178,0.00003795106,0.01218226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4568903,"threshold_uncertainty_score":0.9999385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03131387289424407,"score_gpt":0.2300091558783179,"score_spread":0.1986952829840738,"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."}}