{"id":"W4386432712","doi":"10.1007/978-981-19-9822-5_153","title":"Agent-Based Decentralized Energy Management with Distributed Intelligence for HVAC Control","year":2023,"lang":"en","type":"book-chapter","venue":"Environmental science and engineering","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"HVAC; Scalability; Distributed computing; Computer science; Control engineering; Building management system; Efficient energy use; Multi-agent system; Energy management system; Energy management; Energy (signal processing); Systems engineering; Engineering; Control (management); Air conditioning; Artificial intelligence; Operating system","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":[0.0001990028,0.0004626036,0.0004902684,0.0001787172,0.0002856012,0.0009674648,0.0008478379,0.0006107768,0.002953662],"category_scores_gemma":[0.0003503058,0.0002530676,0.0002960576,0.0003439957,0.0003742633,0.0007151881,0.0005762077,0.001005172,0.000696295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003502983,"about_ca_system_score_gemma":0.0003121619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001068411,"about_ca_topic_score_gemma":0.001524661,"domain_scores_codex":[0.9998597,0.00003235082,0.000006520033,0.00002848339,0.0000581403,0.00001471369],"domain_scores_gemma":[0.9999127,0.00003389557,0.000008873087,0.00001656903,0.0000221375,0.000005841029],"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":[0.0001058012,0.0001142632,0.0001855227,0.0001684368,0.00007461019,0.0001205157,0.00007087063,0.7129644,0.01221119,0.08902747,0.01282883,0.1721281],"study_design_scores_gemma":[0.00001245955,0.00002832359,0.00009768775,0.000008929353,0.000008802896,0.00002869567,0.000009558335,0.9617243,0.001652739,0.02644315,0.009979048,0.00000625784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007263561,0.002207624,0.9577365,0.0003808042,0.0003312256,0.00003706363,0.0000469685,0.0006416782,0.03135464],"genre_scores_gemma":[0.7954047,0.002324106,0.1589206,0.0002320507,0.0003026535,0.0001421983,0.0001583559,0.0001406949,0.0423746],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002953662,"threshold_uncertainty_score":0.00988096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005325048016097454,"score_gpt":0.1559136567642624,"score_spread":0.1505886087481649,"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."}}