{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005851564,0.0007037625,0.0008247768,0.0003652975,0.0003349411,0.0007699152,0.0005605638,0.0005978674,0.00266434],"category_scores_gemma":[0.00109074,0.000399651,0.000319711,0.0007293063,0.000392959,0.0007548831,0.0004481114,0.0005199874,0.0002542298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009856588,"about_ca_system_score_gemma":0.0006619914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004469901,"about_ca_topic_score_gemma":0.003669389,"domain_scores_codex":[0.9996274,0.0001656197,0.00001191612,0.00007911926,0.00005086865,0.00006511687],"domain_scores_gemma":[0.9996316,0.0002299805,0.00005811989,0.00001718018,0.00003646017,0.00002660146],"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":[0.00004813341,0.00002443482,0.0001975396,0.0000282363,0.00001491587,0.00003059721,0.00001478861,0.9822495,0.0005416109,0.00563904,0.0009655977,0.01024554],"study_design_scores_gemma":[0.000006405857,0.00001258614,0.00007063435,0.000001636808,0.000002351144,0.000006307199,0.000009301783,0.9955011,0.0001489451,0.003766375,0.0004726934,0.000001515292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1214134,0.001138559,0.8623664,0.0007274578,0.0001724421,0.00009511166,0.0002963857,0.0004493046,0.01334091],"genre_scores_gemma":[0.911887,0.0006574739,0.07643933,0.0001275604,0.0001057561,0.0001329643,0.0002533234,0.0001159936,0.01028064],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004469901,"threshold_uncertainty_score":0.00891304,"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."}}