{"id":"W2063136778","doi":"10.1109/tsg.2014.2313347","title":"A Distributed Demand Response Control Strategy Using Lyapunov Optimization","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Lyapunov optimization; HVAC; Demand response; Computer science; Mathematical optimization; Control theory (sociology); Optimization problem; Queueing theory; Power control; Engineering; Control (management); Control engineering; Power (physics); Air conditioning; Lyapunov equation; 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":[0.0005750497,0.001173564,0.0009554847,0.0003415543,0.0004028794,0.0009693155,0.0008827788,0.0008523522,0.00227175],"category_scores_gemma":[0.000738061,0.0003168133,0.0004630267,0.0004401386,0.0004708726,0.00045629,0.0009282331,0.0007833921,0.0003767747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006967849,"about_ca_system_score_gemma":0.0008758022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004749895,"about_ca_topic_score_gemma":0.003273177,"domain_scores_codex":[0.9996679,0.00008236698,0.00001624283,0.0000916577,0.00009340472,0.0000484887],"domain_scores_gemma":[0.9996465,0.0001420448,0.00004776891,0.00002060512,0.0001178318,0.00002520913],"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":[0.000064992,0.00007710546,0.000171666,0.00008995391,0.00002904267,0.0001260017,0.00006643826,0.9535504,0.00476159,0.01160437,0.001430355,0.0280281],"study_design_scores_gemma":[0.00001232248,0.00003009501,0.00002102427,0.000001908131,0.000002441068,0.000006131035,0.000003961236,0.9988647,0.0001966098,0.0006427329,0.0002154065,0.000002794998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0134087,0.0002132756,0.9781637,0.000263949,0.00007517039,0.00006910191,0.00002732229,0.000266239,0.007512593],"genre_scores_gemma":[0.9347033,0.0001884468,0.05811033,0.0001649115,0.00005842666,0.0002787013,0.00008076816,0.00004106628,0.006374052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004749895,"threshold_uncertainty_score":0.009444475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01117527032987128,"score_gpt":0.2088614006157398,"score_spread":0.1976861302858685,"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."}}