{"id":"W3115997979","doi":"10.1109/smartgridcomm47815.2020.9302992","title":"Decentralized Microgrid Energy Management: A Multi-agent Correlated Q-learning Approach","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Canada Research Chairs","keywords":"Microgrid; Nash equilibrium; Convergence (economics); Smart grid; Energy management system; Computer science; Energy management; Revenue; Correlated equilibrium; Renewable energy; Mathematical optimization; Competition (biology); Multi-agent system; Operations research; Game theory; Energy (signal processing); Microeconomics; Control (management); Economics; Equilibrium selection; Artificial intelligence; Mathematics; Engineering; Repeated game","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":[0.001431391,0.0006591458,0.001231725,0.0002780678,0.0004804447,0.001013582,0.001538094,0.0009472363,0.001912059],"category_scores_gemma":[0.00221015,0.0004356648,0.0005307533,0.0004810601,0.0008171023,0.0009506679,0.001230621,0.001032053,0.0002161068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007831361,"about_ca_system_score_gemma":0.00134332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004771806,"about_ca_topic_score_gemma":0.003075558,"domain_scores_codex":[0.9993499,0.0002708185,0.00002609442,0.0001352561,0.0001098972,0.0001081113],"domain_scores_gemma":[0.998679,0.0006440537,0.0002011755,0.00009942978,0.0002604125,0.0001159247],"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":[0.00004876467,0.00004185579,0.0004122767,0.00002853669,0.00003545605,0.00005497765,0.00003223449,0.9781014,0.0004106402,0.007758899,0.000433218,0.01264178],"study_design_scores_gemma":[0.000008486151,0.0000129054,0.00003328714,0.000001416762,0.000003471852,0.000005311906,0.000003930494,0.9978675,0.00004780437,0.001905622,0.000108522,0.000001658012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03397099,0.0002803134,0.9614563,0.0003864914,0.00004551561,0.00006513881,0.0000353447,0.0001861055,0.003573844],"genre_scores_gemma":[0.9525003,0.0001443255,0.04527433,0.0001343112,0.00004725562,0.00007635313,0.00003812361,0.00002503065,0.001759941],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004771806,"threshold_uncertainty_score":0.009488046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01820896248369246,"score_gpt":0.2044123409972808,"score_spread":0.1862033785135883,"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."}}