{"id":"W4308630652","doi":"10.3390/su142114446","title":"New Heuristic Methods for Sustainable Energy Performance Analysis of HVAC Systems","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"HVAC; Computer science; Perceptron; Artificial neural network; Thermal comfort; Heuristic; Energy (signal processing); Efficient energy use; Air conditioning; Metaheuristic; Energy consumption; Mathematical optimization; Engineering; Artificial intelligence; Mechanical engineering; Mathematics","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":[0.0007630706,0.0001185497,0.0003111328,0.0003024609,0.0001898385,0.00001948678,0.0001998289,0.00004464555,0.00006161609],"category_scores_gemma":[0.0001553012,0.0001318845,0.0001417519,0.001416342,0.00002006299,0.0001136583,0.00008720822,0.00008562925,1.758001e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008078932,"about_ca_system_score_gemma":0.0002009433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000728599,"about_ca_topic_score_gemma":0.000005582127,"domain_scores_codex":[0.9989817,0.00009487609,0.0002997116,0.0001942989,0.000121417,0.0003079872],"domain_scores_gemma":[0.9989992,0.0001594247,0.000062841,0.0003711905,0.00034925,0.00005811214],"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":[0.00003249028,0.00002104028,0.0007820487,0.0004302447,0.0002312541,7.075977e-7,0.000101683,0.9495231,0.00001077093,0.04229588,0.0005469797,0.006023825],"study_design_scores_gemma":[0.0001574838,0.00007525737,0.0005700652,0.000001405979,0.0002805239,7.535259e-7,0.0007583578,0.9611578,0.0002900866,0.003817717,0.0327466,0.0001439305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03953011,0.0007665503,0.9585637,0.00003136625,0.0001834873,0.0002455891,0.00000741612,0.0001640982,0.0005076459],"genre_scores_gemma":[0.9899999,0.00001387624,0.005731469,0.0000064794,0.00002504718,0.0002684387,0.00005305064,0.00002151066,0.003880196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9528323,"threshold_uncertainty_score":0.5378096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005891004323587449,"score_gpt":0.2583366569507809,"score_spread":0.2524456526271934,"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."}}