{"id":"W4226127937","doi":"10.22215/etd/2022-14935","title":"An Automated Building Energy Model Calibration Workflow to Improve Indoor Climate Controls","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Research Council Canada","keywords":"Workflow; Calibration; Computer science; Automation; Energy (signal processing); Building energy simulation; Energy consumption; Metre; Efficient energy use; Building model; Real-time computing; Simulation; Database; Energy performance; Engineering; Statistics; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001714407,0.001503611,0.0008532554,0.001050395,0.0006715184,0.001446468,0.001275226,0.0005204865,0.003470653],"category_scores_gemma":[0.003885935,0.0006299143,0.001104797,0.000835296,0.0002801965,0.001186388,0.001494868,0.001288806,0.001238112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009985394,"about_ca_system_score_gemma":0.003878317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01605066,"about_ca_topic_score_gemma":0.01622001,"domain_scores_codex":[0.9992223,0.0001565992,0.00005446496,0.0001914444,0.0003034887,0.00007166935],"domain_scores_gemma":[0.9986691,0.000314376,0.0001111301,0.0003504717,0.0004966341,0.00005820853],"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":[0.00009057832,0.0001940333,0.003853169,0.0001559213,0.00005293928,0.0001475558,0.0002564165,0.7725677,0.00984623,0.007345091,0.004719941,0.2007705],"study_design_scores_gemma":[0.00001351472,0.00002026425,0.0007802826,0.00001726239,0.0000108456,0.00002176981,0.00004750961,0.9868068,0.004565914,0.003150464,0.004547406,0.0000178785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0137894,0.00006245451,0.9746559,0.0001239296,0.00003344742,0.0001647191,0.0005195768,0.007684744,0.002965912],"genre_scores_gemma":[0.2902476,0.0001723097,0.7042665,0.00005251392,0.00001901515,0.0002924221,0.002119337,0.000740471,0.002089836],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01605066,"threshold_uncertainty_score":0.03191447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004726642754417878,"score_gpt":0.24112791993993,"score_spread":0.2364012771855121,"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."}}