{"id":"W4389558906","doi":"10.1088/1742-6596/2654/1/012099","title":"Model predictive control for demand response in all-electric school buildings","year":2023,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Concordia University","funders":"","keywords":"Flexibility (engineering); Electricity; Model predictive control; Demand response; Peak demand; Thermal comfort; Electricity price; Computer science; Automotive engineering; Environmental science; Environmental economics; Control (management); Engineering; Economics; Electrical engineering; Meteorology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003596864,0.0005285227,0.0005319744,0.0002031056,0.0003051961,0.0006508649,0.0005724077,0.0003040249,0.001637493],"category_scores_gemma":[0.0006354465,0.0002439503,0.0002538677,0.0002893354,0.0002608926,0.000273012,0.0003507111,0.0005092841,0.0001439386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006919103,"about_ca_system_score_gemma":0.0005530938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02950363,"about_ca_topic_score_gemma":0.02564784,"domain_scores_codex":[0.9998073,0.00005484549,0.00000686893,0.00003212283,0.00005925831,0.00003962148],"domain_scores_gemma":[0.9997309,0.000140713,0.00004015824,0.000013309,0.00006242043,0.00001246506],"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":[0.0000725632,0.00003816049,0.0003454485,0.00003244874,0.00001410762,0.00003143792,0.00002074631,0.9889022,0.001490972,0.0007154874,0.0002549523,0.008081411],"study_design_scores_gemma":[0.000005860907,0.00001994788,0.0001997075,0.000001378253,0.000003175384,0.000001834932,0.000005863663,0.9990276,0.0003363587,0.0002712982,0.0001249913,0.000002011498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.371414,0.0005894265,0.6059412,0.0003867403,0.00009624174,0.00009901093,0.00018414,0.001200344,0.02008892],"genre_scores_gemma":[0.9975389,0.00003857999,0.001523648,0.00000993778,0.000003316037,0.0000121232,0.00002077054,0.000006033228,0.0008466563],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02950363,"threshold_uncertainty_score":0.05866379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02402631845912081,"score_gpt":0.2450074941998578,"score_spread":0.220981175740737,"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."}}