{"id":"W1910212912","doi":"10.1109/acc.1995.532750","title":"Energy-efficient operation of HVAC systems using neural network based decentralized controllers","year":2005,"lang":"en","type":"article","venue":"","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Setpoint; Artificial neural network; HVAC; Controller (irrigation); Computer science; Control engineering; Control theory (sociology); Energy (signal processing); Decentralised system; Control (management); Engineering; Artificial intelligence; Air conditioning; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003031239,0.0002899499,0.000282154,0.00013624,0.0002401244,0.0003160724,0.0003673993,0.0003039099,0.0009664466],"category_scores_gemma":[0.0006094601,0.0001714216,0.000155651,0.0001277725,0.0002355114,0.0003946116,0.0002782155,0.0004401235,0.0001376999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003151961,"about_ca_system_score_gemma":0.0003589169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00231291,"about_ca_topic_score_gemma":0.004150319,"domain_scores_codex":[0.9998179,0.00003908989,0.000008672325,0.00003328144,0.00007584243,0.00002526014],"domain_scores_gemma":[0.9997719,0.000070311,0.00004504128,0.00002740702,0.00007142914,0.00001392181],"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":[0.0002274539,0.0001594372,0.0006744097,0.00008209192,0.00004029702,0.00005760969,0.00005506117,0.842724,0.06971739,0.001906299,0.000587286,0.08376864],"study_design_scores_gemma":[0.00003890337,0.0001212941,0.0004741288,0.000002830724,0.000006924976,0.00001417472,0.00000563228,0.9883863,0.01001053,0.0005344997,0.0003982611,0.000006465347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2383604,0.000264013,0.755483,0.0002113099,0.00005567262,0.00007055601,0.00003241195,0.0007400558,0.004782607],"genre_scores_gemma":[0.9805155,0.00004461674,0.01810752,0.00001846398,0.00001368065,0.00002860546,0.00001854341,0.00001039831,0.00124276],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00231291,"threshold_uncertainty_score":0.004598856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00933163485070658,"score_gpt":0.1949937264352423,"score_spread":0.1856620915845357,"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."}}