{"id":"W4319047955","doi":"","title":"Prediction of HVAC System Parameters Using Deep Learning","year":2022,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"British Columbia Institute of Technology","funders":"","keywords":"HVAC; Computer science; Artificial intelligence; Deep learning; Machine learning; Engineering; Air conditioning; 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":[0.0002559974,0.000654265,0.0003343997,0.0003318878,0.000207435,0.0004699599,0.0003710295,0.0005160858,0.0008501525],"category_scores_gemma":[0.0007609682,0.0003509841,0.0003583149,0.0002838192,0.0001902885,0.0005239082,0.0003032257,0.0007341138,0.0001651325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007588898,"about_ca_system_score_gemma":0.0006794044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0238456,"about_ca_topic_score_gemma":0.02040252,"domain_scores_codex":[0.9998987,0.00001709346,0.000006584783,0.00002814488,0.00002886804,0.00002072364],"domain_scores_gemma":[0.9997734,0.0001110623,0.00003341993,0.00001729632,0.00005529998,0.000009586977],"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":[0.00001287716,0.00001760505,0.0006722388,0.000008963349,0.000008926413,0.0000103863,0.000005739739,0.9883077,0.0008758229,0.000119788,0.0001209412,0.009839064],"study_design_scores_gemma":[4.880611e-7,0.000003069593,0.0001610041,7.024395e-7,8.94119e-7,0.000001095671,8.208708e-7,0.999436,0.0002832822,0.00008794019,0.00002402361,7.745692e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.488123,0.0006343486,0.5038153,0.0003159337,0.00006761328,0.00004149253,0.0004757283,0.001473069,0.00505352],"genre_scores_gemma":[0.9887114,0.00007455415,0.01010773,0.00001690144,0.000005985532,0.00001672407,0.0001998627,0.00001226269,0.0008544921],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0238456,"threshold_uncertainty_score":0.04741365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01615756477769193,"score_gpt":0.1962533541502448,"score_spread":0.1800957893725529,"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."}}