{"id":"W2243107379","doi":"10.1109/fskd.2015.7382083","title":"A statistical model for predicting power demand peaks in power systems","year":2015,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Independent Electricity System Operator","keywords":"Electricity; Computer science; Peak demand; Software deployment; Environmental economics; Fiscal year; Work (physics); Commodity; Electricity generation; Power (physics); Operations research; Business; Finance; Economics; Engineering; Electrical engineering; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001597306,0.0007989171,0.0007986778,0.001146677,0.0004288671,0.00089897,0.001218553,0.000834291,0.001473873],"category_scores_gemma":[0.005325737,0.0004960352,0.0007501327,0.001486676,0.0004508705,0.001237857,0.0004798659,0.001421387,0.0006250145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001121834,"about_ca_system_score_gemma":0.0009686222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02574633,"about_ca_topic_score_gemma":0.02302823,"domain_scores_codex":[0.9992769,0.0002433975,0.00005359271,0.0001755335,0.0001570731,0.0000934603],"domain_scores_gemma":[0.9975539,0.001761853,0.0002319619,0.0001056862,0.0002888602,0.00005768775],"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":[0.00005090496,0.00005998917,0.005478339,0.00002171319,0.00003523767,0.00003532933,0.00002280637,0.9640234,0.0002698569,0.002955858,0.001126079,0.02592058],"study_design_scores_gemma":[0.000001187156,0.00000692811,0.000266039,0.000001042177,0.000001732601,0.000003602172,0.000002096813,0.9986785,0.00003587744,0.0009219337,0.000079415,0.000001664098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1099904,0.0006211202,0.8833049,0.0009106066,0.0001072882,0.00009624405,0.001316045,0.001583382,0.002070017],"genre_scores_gemma":[0.9397511,0.0004906902,0.05402699,0.0001635947,0.0001368773,0.0001776815,0.001741989,0.00007852011,0.003432552],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02574633,"threshold_uncertainty_score":0.05119294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02508364262876674,"score_gpt":0.2376160627073734,"score_spread":0.2125324200786066,"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."}}