{"id":"W3044575686","doi":"10.1016/j.jobe.2020.101639","title":"Parameter estimation of resistor-capacitor models for building thermal dynamics using the unscented Kalman filter","year":2020,"lang":"en","type":"article","venue":"Journal of Building Engineering","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Comisión Nacional de Investigación Científica y Tecnológica; Universidad de los Andes; Fondo Nacional de Desarrollo Científico y Tecnológico; Concordia University; University of Alberta","keywords":"Kalman filter; Predictability; Resistor; Computer science; Building model; Extended Kalman filter; Estimation theory; Online model; Control engineering; Control theory (sociology); Control (management); Engineering; Simulation; Artificial intelligence; Algorithm; Voltage","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":[0.000376479,0.0004724927,0.0006679113,0.0002550047,0.000281966,0.0007322036,0.0006798218,0.0006846893,0.001511104],"category_scores_gemma":[0.001749444,0.000480324,0.0004659227,0.0003341117,0.0002957069,0.001029069,0.0003889174,0.0009672536,0.00047488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005101371,"about_ca_system_score_gemma":0.0008609823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01707975,"about_ca_topic_score_gemma":0.02102861,"domain_scores_codex":[0.9998105,0.00004901639,0.00001249809,0.00006043442,0.00004815688,0.00001933273],"domain_scores_gemma":[0.9996307,0.0001841979,0.00004884915,0.00004539658,0.00008107709,0.000009850449],"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":[0.0000459225,0.0000215129,0.000600673,0.00004837412,0.00003302618,0.00002091951,0.00004367254,0.9732947,0.003030534,0.001620542,0.0003638899,0.02087615],"study_design_scores_gemma":[0.000002564969,0.000005023852,0.000176485,0.000002574936,0.000003991297,0.000003448414,0.000002906454,0.9987348,0.0005249294,0.000357441,0.0001820619,0.00000384688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03278554,0.0001811326,0.9652404,0.00008650922,0.00002973953,0.00001578006,0.0001028832,0.0005605966,0.0009974457],"genre_scores_gemma":[0.9398327,0.0002437219,0.05646821,0.00003840284,0.00001768809,0.00005968188,0.000285974,0.00007651634,0.002977133],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01707975,"threshold_uncertainty_score":0.03396064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02100022690975244,"score_gpt":0.220467285289479,"score_spread":0.1994670583797266,"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."}}