{"id":"W3106066138","doi":"10.1109/tsg.2020.3037066","title":"Deep Reinforcement Learning for Demand Response in Distribution Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":129,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Demand response; Reinforcement learning; Computer science; Scalability; Scheduling (production processes); Load management; Electricity; Mathematical optimization; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00106243,0.0006516681,0.0007314717,0.0002350731,0.0003028891,0.0005488717,0.0007902255,0.0007288665,0.001744054],"category_scores_gemma":[0.003482185,0.0003765038,0.0002646652,0.0002939221,0.0007291232,0.0007389073,0.0006827742,0.001588831,0.00019333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001495737,"about_ca_system_score_gemma":0.001173689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01004728,"about_ca_topic_score_gemma":0.009981765,"domain_scores_codex":[0.9996179,0.0001578906,0.0000134495,0.00007095534,0.00006694049,0.00007278132],"domain_scores_gemma":[0.9988349,0.0008036367,0.0001035929,0.00004935309,0.0001558554,0.00005270481],"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":[0.00002844105,0.00002358255,0.000271545,0.00001487855,0.000008504183,0.0000168716,0.00001431954,0.9862774,0.0002797346,0.003431146,0.0003706202,0.009262924],"study_design_scores_gemma":[0.000002179296,0.000003012402,0.00001485127,7.114686e-7,5.553439e-7,7.872326e-7,9.38263e-7,0.9988589,0.00005053625,0.001027131,0.00003975827,5.565337e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04662343,0.0003168102,0.9486595,0.0005624703,0.00004402148,0.00003414408,0.00005713228,0.0004912508,0.003211217],"genre_scores_gemma":[0.9700423,0.0001111826,0.02715436,0.00009891217,0.00001837196,0.00006302163,0.00006592648,0.00003544754,0.002410429],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01004728,"threshold_uncertainty_score":0.01997763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01234859639463223,"score_gpt":0.2089434403846324,"score_spread":0.1965948439900002,"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."}}