{"id":"W2172966071","doi":"10.3390/app5041134","title":"An Efficient Power Scheduling Scheme for Residential Load Management in Smart Homes","year":2015,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":132,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Dalhousie University","funders":"King Saud University","keywords":"Knapsack problem; Mathematical optimization; Regret; Maximization; Computer science; Electricity; Scheduling (production processes); Scheme (mathematics); Convergence (economics); Engineering; Mathematics; Electrical engineering; Economics","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":[0.0003791971,0.0004798584,0.0005969626,0.0001950007,0.0003863309,0.0004829842,0.0007053417,0.0003556611,0.001948376],"category_scores_gemma":[0.0005808674,0.0001809361,0.0002555704,0.0004400268,0.0002709182,0.0007499301,0.0004254717,0.000432331,0.0003041418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000479662,"about_ca_system_score_gemma":0.0006618188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001447369,"about_ca_topic_score_gemma":0.002762882,"domain_scores_codex":[0.9997856,0.00006588997,0.00001091638,0.00005291511,0.00005488234,0.00002986065],"domain_scores_gemma":[0.9998558,0.00004873822,0.00003139968,0.00002070193,0.00003119588,0.00001219362],"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":[0.00007403262,0.00007204959,0.0002955256,0.00009262483,0.00002006079,0.00005585643,0.00006311353,0.9059119,0.003971157,0.02161275,0.002010889,0.06582005],"study_design_scores_gemma":[0.000005480799,0.00001770655,0.0000462068,0.000001969589,0.000003150373,0.00001190252,0.000006751716,0.9964341,0.0003540896,0.002552575,0.000563647,0.000002393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01410569,0.0001433135,0.9824388,0.00008171201,0.00003716746,0.00004764824,0.000035094,0.0001896248,0.002920871],"genre_scores_gemma":[0.8856941,0.000238448,0.1112708,0.00006036643,0.00005342154,0.00009760926,0.00007749051,0.00004042334,0.002467412],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001948376,"threshold_uncertainty_score":0.006518006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0198468052039694,"score_gpt":0.2531211277162739,"score_spread":0.2332743225123045,"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."}}