{"id":"W4386432857","doi":"10.1007/978-981-19-9822-5_155","title":"A Model-Based Predictive Control Method for Thermal Environment in Low-Energy Buildings","year":2023,"lang":"en","type":"book-chapter","venue":"Environmental science and engineering","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Thermal comfort; Model predictive control; Environmental science; Energy conservation; Energy consumption; Humidity; Thermal; Efficient energy use; Artificial neural network; Computer science; Calibration; Thermal inertia; Control (management); Engineering; Meteorology; Geography; Artificial intelligence; Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002269769,0.0006081556,0.0008088694,0.0001821061,0.0004197505,0.0007306743,0.0009264872,0.0006853712,0.00431461],"category_scores_gemma":[0.0005140871,0.0003302106,0.000481492,0.0003545306,0.0003924383,0.0006155628,0.0004345872,0.001027993,0.0005854107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00037116,"about_ca_system_score_gemma":0.0004304543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008396188,"about_ca_topic_score_gemma":0.007895402,"domain_scores_codex":[0.9998834,0.00002656848,0.000005190906,0.00002389521,0.00005087906,0.000009952002],"domain_scores_gemma":[0.9998983,0.00005554555,0.000006380562,0.00000848649,0.00002732944,0.00000391411],"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":[0.00005540104,0.00004442752,0.00007890945,0.0001596635,0.00002904605,0.0000598963,0.00004770415,0.8655755,0.004769651,0.009364181,0.003191169,0.1166244],"study_design_scores_gemma":[0.000003096099,0.00001313077,0.00003499938,0.000004095451,0.00000426226,0.000006419526,0.000002396735,0.9974989,0.0003550992,0.001237754,0.0008372418,0.000002617651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004918305,0.0009296556,0.9863656,0.0001110608,0.0002406076,0.0000287362,0.00003546904,0.0005216032,0.006849042],"genre_scores_gemma":[0.7544415,0.002007883,0.2109707,0.0002083284,0.0002967991,0.000295745,0.0002158908,0.0002652052,0.03129794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008396188,"threshold_uncertainty_score":0.01669461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005239129896350528,"score_gpt":0.1687635760377692,"score_spread":0.1635244461414187,"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."}}