{"id":"W4240594140","doi":"10.32920/ryerson.14646717","title":"Operational optimization of residential HVAC system using model predictive control strategy planning","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"TRNSYS; HVAC; Energy consumption; Controller (irrigation); Model predictive control; Automotive engineering; Engineering; Air conditioning; Electricity; Efficient energy use; Simulation; Thermal comfort; Control engineering; Computer science; Energy (signal processing); Control (management); Mechanical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000450345,0.000621222,0.0006607418,0.0003273879,0.0004072486,0.001022809,0.00049822,0.0005280069,0.001549263],"category_scores_gemma":[0.0007075574,0.0003087193,0.0004796099,0.0003350124,0.0003592048,0.0003009301,0.0004825019,0.0005873033,0.0001437315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006881409,"about_ca_system_score_gemma":0.001293706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02510709,"about_ca_topic_score_gemma":0.01464827,"domain_scores_codex":[0.9997939,0.00005556396,0.000008653809,0.00003172831,0.00006536036,0.00004473299],"domain_scores_gemma":[0.9997514,0.0001222334,0.000035015,0.00001032732,0.00006505524,0.00001590376],"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":[0.00002131932,0.00001761389,0.0002117441,0.00002503625,0.000006658889,0.00003046904,0.00001325166,0.994422,0.0005092442,0.0004028505,0.0001396856,0.004200152],"study_design_scores_gemma":[0.000003177117,0.00001760397,0.00008616806,0.000001301151,0.000002390443,0.000002045241,0.000006276903,0.9995443,0.0001594838,0.000102958,0.00007307564,0.000001205689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3658237,0.001250357,0.586417,0.0006002875,0.0001072817,0.0002793126,0.0002268626,0.001149099,0.04414624],"genre_scores_gemma":[0.9847881,0.000141546,0.01275041,0.00003018169,0.000006357453,0.00008638433,0.00007268208,0.00001363151,0.002110743],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02510709,"threshold_uncertainty_score":0.04992187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01915243405637687,"score_gpt":0.2331457160880599,"score_spread":0.213993282031683,"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."}}