{"id":"W4307525929","doi":"10.32920/21408591.v1","title":"Appliance Scheduling Optimization in Smart Home Networks","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Photovoltaic system; Smart grid; Scheduling (production processes); Computer science; Schedule; Electricity; Home automation; Mathematical optimization; Integer programming; Grid; Real-time computing; Engineering; Electrical engineering; Telecommunications; Algorithm; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000217738,0.0002508609,0.0002519276,0.0002113063,0.00003257072,0.00006035853,0.000347221,0.0001581522,0.0008203776],"category_scores_gemma":[0.000005198706,0.000314952,0.00005989811,0.0002942439,0.000009379592,0.00005613903,0.0007922892,0.000722726,0.0000155485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003683566,"about_ca_system_score_gemma":0.00001250776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006401214,"about_ca_topic_score_gemma":0.00004659169,"domain_scores_codex":[0.9988251,0.00002389744,0.0003136596,0.0003570431,0.0001829294,0.0002974123],"domain_scores_gemma":[0.9994069,0.00002164865,0.00003696958,0.0004804499,0.00001129944,0.00004270032],"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":[0.000002119557,0.000009932883,0.0008543227,0.00009955597,0.00003423863,0.000008966294,0.00001971982,0.9972528,7.342418e-7,0.0005303786,0.000687174,0.0005001092],"study_design_scores_gemma":[0.0001368387,0.000003026689,0.0007456936,0.0000454391,0.000009346184,3.455865e-7,0.00003054702,0.9944819,0.00000501359,0.00008332523,0.004129066,0.0003294181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00418112,0.0007246289,0.9575977,0.00005239433,0.003482914,0.000333533,0.000002332225,0.0008062224,0.03281916],"genre_scores_gemma":[0.8438663,0.002370977,0.1489178,0.000215322,0.0007766797,0.001381684,0.0005548873,0.000260216,0.001656157],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8396852,"threshold_uncertainty_score":0.9999303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01065360652753309,"score_gpt":0.2043330758925649,"score_spread":0.1936794693650318,"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."}}