{"id":"W3108203829","doi":"10.1109/epe51172.2020.9269163","title":"Intelligent Scheduling of Heat Pump to Minimize the Cost of Electricity","year":2020,"lang":"en","type":"article","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heat pump; Particle swarm optimization; Energy conservation; Computer science; Electricity; Scheduling (production processes); Metaheuristic; Energy consumption; Genetic algorithm; Mathematical optimization; Engineering; Mechanical engineering; Algorithm; Mathematics; Electrical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003102755,0.0004717508,0.0007487337,0.000306185,0.0004013904,0.000844033,0.0004676188,0.0004970765,0.001761437],"category_scores_gemma":[0.0006020615,0.000247543,0.0003186873,0.0003171714,0.0002844363,0.000521386,0.0002889401,0.0003970357,0.0001582249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006109955,"about_ca_system_score_gemma":0.001136243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003399605,"about_ca_topic_score_gemma":0.003935043,"domain_scores_codex":[0.999792,0.00005668418,0.000008084258,0.0000470366,0.00004408498,0.00005207317],"domain_scores_gemma":[0.9998267,0.00006958805,0.00004015773,0.00001027513,0.00002952771,0.00002365626],"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":[0.0001443476,0.0000900642,0.0006205943,0.00005483318,0.00002959146,0.00005825017,0.00003111441,0.9615452,0.00860327,0.004537781,0.0008835203,0.02340141],"study_design_scores_gemma":[0.00001009103,0.00004269388,0.0002434242,0.000001527245,0.000006061768,0.000008495831,0.00001046413,0.9974939,0.00104782,0.000839172,0.0002936159,0.000002810828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2768053,0.0004858783,0.709708,0.0003367668,0.000140845,0.0002002815,0.000110003,0.0005306241,0.01168228],"genre_scores_gemma":[0.97405,0.0001020468,0.02393676,0.0000209044,0.00001693347,0.00004827951,0.00003289164,0.00002158621,0.001770523],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003399605,"threshold_uncertainty_score":0.006759644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02366640698211257,"score_gpt":0.2197586692455916,"score_spread":0.196092262263479,"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."}}