{"id":"W2575468172","doi":"10.24251/hicss.2017.365","title":"Learning to Shift Thermostatically Controlled Loads","year":2017,"lang":"en","type":"article","venue":"Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"News aggregator; Demand response; Regret; Computer science; A priori and a posteriori; Key (lock); Load management; Mathematical optimization; Grid; Adversarial system; Load balancing (electrical power); Thompson sampling; Artificial intelligence; Machine learning; Electricity; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.004064686,0.0008265596,0.00111542,0.0008191096,0.001773512,0.002065622,0.01286509,0.00023796,0.0001077514],"category_scores_gemma":[0.0003348066,0.0005396269,0.0005365755,0.0008528059,0.001891151,0.002325972,0.001764278,0.0006855283,0.00009632765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005853677,"about_ca_system_score_gemma":0.0002843665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002491176,"about_ca_topic_score_gemma":0.00002372297,"domain_scores_codex":[0.9912516,0.00004919893,0.00174347,0.001314559,0.004696912,0.0009442286],"domain_scores_gemma":[0.9927226,0.0002121358,0.002095937,0.0004502894,0.004216497,0.0003025629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002696324,0.00008667973,0.004722782,0.0002087223,0.0002233566,0.000001318126,0.002040485,0.003569336,0.00140231,0.9854135,0.001581001,0.0004808569],"study_design_scores_gemma":[0.003357626,0.001714054,0.01786578,0.009108656,0.0001611083,0.00008669747,0.9413388,0.01548574,0.003762655,0.003395385,0.002070321,0.001653225],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1307321,0.000013221,0.00006916686,0.005867876,0.004858551,0.00107422,0.0001991292,0.0002474001,0.8569384],"genre_scores_gemma":[0.9963406,0.00002559822,0.0003685145,0.0001250187,0.0004886162,0.0002152606,0.000002150827,0.00005220764,0.002382031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9820181,"threshold_uncertainty_score":0.9997056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03144026382206932,"score_gpt":0.281180233242323,"score_spread":0.2497399694202537,"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."}}