{"id":"W2955939480","doi":"10.1109/epec47565.2019.9074777","title":"Minimizing Distribution System Power Loss Using Behind-the-Meter Type 3 Generators","year":2019,"lang":"en","type":"article","venue":"","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Installation; Renewable energy; Distributed generation; Particle swarm optimization; Metre; Electric power system; Electricity meter; Power loss; Computer science; Reduction (mathematics); Mathematical optimization; Power (physics); Engineering; Automotive engineering; Electrical engineering; Mathematics","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":[0.0002260862,0.0006285662,0.0004179902,0.0003090756,0.0001561812,0.0005478708,0.0003927898,0.0003010067,0.0008255108],"category_scores_gemma":[0.0003883566,0.0001747227,0.0003467494,0.0003169245,0.0001921652,0.0005345871,0.0003175448,0.000252863,0.0001703092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000367622,"about_ca_system_score_gemma":0.0002741131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009692836,"about_ca_topic_score_gemma":0.00196204,"domain_scores_codex":[0.9998547,0.00004401351,0.000004463877,0.00002124262,0.00005314453,0.00002251262],"domain_scores_gemma":[0.9998579,0.0000503281,0.0000413304,0.00001506473,0.00002438824,0.00001088529],"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":[0.0002052578,0.0001293986,0.002209926,0.00005226664,0.00004384409,0.0001198472,0.00003507176,0.8953208,0.0180931,0.001795548,0.0005855834,0.08140942],"study_design_scores_gemma":[0.00002386579,0.0002282522,0.001052476,0.000004388784,0.00002046934,0.00005046522,0.00001060368,0.9928559,0.003850867,0.001286858,0.0006101251,0.00000574271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3291671,0.0004141826,0.6615822,0.000110101,0.00002985191,0.00005335801,0.00007580686,0.0005224501,0.008044884],"genre_scores_gemma":[0.9713404,0.0001038902,0.02721854,0.00001875147,0.00001018002,0.00001012574,0.00003573913,0.00003704039,0.001225475],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009692836,"threshold_uncertainty_score":0.002761662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006882395495811009,"score_gpt":0.1952841107209627,"score_spread":0.1884017152251517,"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."}}