{"id":"W2123524465","doi":"10.1109/nafips.2004.1336248","title":"Evolutionary algorithms for multi-objective optimization in HVAC system control strategy","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"HVAC; Evolutionary algorithm; Multi-objective optimization; Mathematical optimization; Sorting; Computer science; Genetic algorithm; Optimization problem; Simulated annealing; Energy consumption; Energy (signal processing); Pareto principle; Engineering; Algorithm; Mathematics; Air conditioning; Mechanical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004302044,0.0002359574,0.0002677576,0.0002486439,0.0002919983,0.00007239266,0.0002945403,0.0001980958,9.677809e-7],"category_scores_gemma":[0.0001120337,0.0002102119,0.00008950517,0.0006230517,0.00005867685,0.001709248,0.00001763837,0.0001932176,0.000001744112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004003445,"about_ca_system_score_gemma":0.0002175917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009507559,"about_ca_topic_score_gemma":0.00001643562,"domain_scores_codex":[0.9984331,0.00003828163,0.000770179,0.0001459185,0.0002845252,0.0003280528],"domain_scores_gemma":[0.9988176,0.00004812285,0.0003432926,0.0001901684,0.0005515203,0.00004928845],"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":[0.00004009439,0.00003254467,0.0001130914,0.0004404403,0.00002854277,2.544111e-7,0.001554271,0.9956419,0.00003350441,0.0003019017,0.0001530244,0.001660473],"study_design_scores_gemma":[0.002149847,0.00004872413,0.0002860258,0.0007771488,0.00003674814,0.00001548997,0.00149962,0.9931103,0.001539217,0.0001997939,0.00007523201,0.000261841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02092045,0.0007755167,0.9738862,0.00008564301,0.0009989209,0.0009980287,0.0001798504,0.0005255666,0.001629832],"genre_scores_gemma":[0.960517,0.00001231505,0.03906113,0.00004680204,0.0001109692,0.0001307319,0.00005820585,0.00003277448,0.00003009434],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9395965,"threshold_uncertainty_score":0.8572193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01032832596760207,"score_gpt":0.2253361381911341,"score_spread":0.215007812223532,"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."}}