{"id":"W4251344196","doi":"10.32920/ryerson.14656659.v1","title":"Demand Side Simulation: Architecture and Performance","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Relational database management system; Context (archaeology); Smart grid; Demand response; Energy consumption; Efficient energy use; Simulation; Grid; On demand; Architecture; Embedded system; Electricity; Database; Engineering; Relational database","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":[0.001285633,0.0008329898,0.0006357024,0.0005011115,0.0004967983,0.0023728,0.00196208,0.0009844102,0.006027763],"category_scores_gemma":[0.003786229,0.0006012314,0.0004952944,0.0006572558,0.0004901042,0.002242665,0.001707729,0.001017563,0.002009389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009610009,"about_ca_system_score_gemma":0.001188291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006065276,"about_ca_topic_score_gemma":0.002980759,"domain_scores_codex":[0.9990298,0.0003174903,0.00007109397,0.0001515753,0.0003406708,0.0000893684],"domain_scores_gemma":[0.998098,0.0006277203,0.00007198894,0.0006344661,0.0004184855,0.0001493728],"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":[0.001318539,0.0006385605,0.01310436,0.0003276448,0.000199286,0.0002233364,0.0004717693,0.7530748,0.02854056,0.050114,0.01119411,0.140793],"study_design_scores_gemma":[0.00002380889,0.00003269516,0.0002223063,0.000005530009,0.00001001899,0.00003158295,0.00001823493,0.9894869,0.004149424,0.003220179,0.002790233,0.000008997611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1081513,0.0005551415,0.8397219,0.0008752372,0.0001351816,0.0003441733,0.0007255154,0.02451713,0.02497437],"genre_scores_gemma":[0.8005247,0.0006683025,0.1870601,0.0001749332,0.00006698201,0.00026556,0.002047878,0.001609236,0.00758242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006065276,"threshold_uncertainty_score":0.02016485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009062923102969305,"score_gpt":0.2004544728180119,"score_spread":0.1913915497150426,"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."}}