{"id":"W2969475691","doi":"10.1016/j.enbuild.2019.109382","title":"Energy calibration of HVAC sub-system model using sensitivity analysis and meta-heuristic optimization","year":2019,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hydro-Québec; École de Technologie Supérieure","funders":"Fonds de recherche du Québec – Nature et technologies; Canada Foundation for Innovation; Compute Canada","keywords":"HVAC; ASHRAE 90.1; Calibration; Sensitivity (control systems); Heuristic; Energy (signal processing); Engineering; Simulation; Computer science; Air conditioning; Electronic engineering; Mechanical engineering; Artificial intelligence; Statistics; Mathematics","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":[0.001577657,0.001033137,0.001213326,0.001104906,0.0005635338,0.001261518,0.0009110681,0.001516357,0.002497473],"category_scores_gemma":[0.003551297,0.0008279146,0.001216816,0.0006182115,0.0005457758,0.001012569,0.0007786323,0.001455127,0.0002160372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001032194,"about_ca_system_score_gemma":0.001144941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01158628,"about_ca_topic_score_gemma":0.005563363,"domain_scores_codex":[0.9995033,0.000264199,0.00001955795,0.00006120529,0.00008773217,0.00006413979],"domain_scores_gemma":[0.9983476,0.001209786,0.0001065981,0.00008648674,0.0002255278,0.00002387471],"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":[0.000009777859,0.000006493698,0.00007315645,0.0000107397,0.000008482336,0.000005574019,0.000003964978,0.9987581,0.0001229468,0.0001878472,0.00003637564,0.0007766089],"study_design_scores_gemma":[0.000002950065,0.000009419838,0.00006343518,0.000003509873,0.000004897169,0.000002207999,0.000003703228,0.9994911,0.0001606451,0.0002217389,0.00003425526,0.000002141689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.294102,0.0008568431,0.6790944,0.0005418721,0.0001163426,0.0002839673,0.0006393152,0.0009001013,0.02346522],"genre_scores_gemma":[0.9799344,0.00009427765,0.01849487,0.00004421916,0.000009448443,0.0001178054,0.0001210249,0.00004417087,0.001139756],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01158628,"threshold_uncertainty_score":0.02303767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00871584109653744,"score_gpt":0.1826071815667877,"score_spread":0.1738913404702502,"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."}}