{"id":"W4410796102","doi":"10.1016/j.enbuild.2025.115938","title":"Semantic Digital Twinning for Cost-Optimal HVAC Operation: Real-Time Application to a House with Smart Thermostats and PV/Battery under a Time-of-Use Tariff","year":2025,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Mitacs; Hydro-Québec; Concordia University","keywords":"Thermostat; HVAC; Battery (electricity); Automotive engineering; Computer science; Stove; Tariff; Photovoltaic system; Reliability engineering; Engineering; Electrical engineering; Air conditioning; Mechanical engineering; Business","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005665068,0.0004761109,0.0003242497,0.0002940396,0.0002569334,0.000613805,0.0006503282,0.000263653,0.001402472],"category_scores_gemma":[0.001012091,0.0001876058,0.0002437118,0.0002977466,0.0004527574,0.0008589964,0.0007908217,0.0003695532,0.0001353384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004701803,"about_ca_system_score_gemma":0.0005132009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002650182,"about_ca_topic_score_gemma":0.002784144,"domain_scores_codex":[0.9997376,0.00006254348,0.00001336299,0.00005602598,0.00009324551,0.00003732832],"domain_scores_gemma":[0.9997336,0.00008389778,0.00003669769,0.0000602014,0.00005615071,0.00002947359],"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":[0.000726443,0.0002675198,0.004272133,0.0001147539,0.00003703207,0.0002592379,0.0002805425,0.7453099,0.05201989,0.009406798,0.0008581069,0.1864476],"study_design_scores_gemma":[0.00001160058,0.0001044854,0.000524849,0.000002324328,0.00000829662,0.00002915671,0.00003644262,0.9880772,0.009572556,0.001096615,0.0005281197,0.000008391904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3376153,0.00008879536,0.6564854,0.00007550178,0.00003375599,0.00007542808,0.0000528135,0.001561949,0.004010967],"genre_scores_gemma":[0.9637372,0.00001847302,0.03581689,0.000008392851,0.000002752423,0.00001422944,0.00002758865,0.0000331234,0.0003413839],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002650182,"threshold_uncertainty_score":0.005269527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004379055448197821,"score_gpt":0.19428858174977,"score_spread":0.1899095263015722,"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."}}