{"id":"W4376880088","doi":"10.1016/j.energy.2023.127839","title":"Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector","year":2023,"lang":"en","type":"article","venue":"Energy","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke; Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Norsk Hydro","keywords":"Thermostat; Model predictive control; Calibration; Controller (irrigation); Renewable energy; Electricity; Flexibility (engineering); Time horizon; Computer science; Environmental science; Engineering; Control (management); Statistics; Mathematics; Mathematical optimization","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":[0.000784244,0.0007136705,0.0006272084,0.0006722808,0.0004237563,0.0006236397,0.001002308,0.001172932,0.00246956],"category_scores_gemma":[0.002364999,0.000338206,0.0007797814,0.001007029,0.0002636927,0.0008131426,0.0005198601,0.0007654046,0.001561916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007058164,"about_ca_system_score_gemma":0.001017362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03084243,"about_ca_topic_score_gemma":0.03825036,"domain_scores_codex":[0.9995843,0.0001036062,0.00002932006,0.0001320182,0.0001024112,0.00004848784],"domain_scores_gemma":[0.9990335,0.0002567187,0.00006693364,0.0002973661,0.0002873049,0.00005823754],"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":[0.0005931823,0.0008320422,0.05134749,0.0003657754,0.0003282375,0.0003515822,0.0001105171,0.808911,0.005310895,0.001973125,0.07755762,0.05231864],"study_design_scores_gemma":[0.0001426926,0.0001099829,0.05239886,0.00003740489,0.0000450454,0.00007213234,0.0001367295,0.9250441,0.00434449,0.0023416,0.01526733,0.00005959753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7703386,0.0004374897,0.03823123,0.00143383,0.0003453791,0.0001468698,0.1715548,0.008939156,0.008572718],"genre_scores_gemma":[0.8243145,0.0001365356,0.01667741,0.0001510947,0.00005614199,0.00009828703,0.1563559,0.0002616155,0.001948563],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03084243,"threshold_uncertainty_score":0.06132579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00921962835408288,"score_gpt":0.2498639890540267,"score_spread":0.2406443606999439,"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."}}