{"id":"W3210437470","doi":"10.1109/isie45552.2021.9576331","title":"A Reinforcement Learning based Energy Management System for a PV and Battery Connected Microgrid System","year":2021,"lang":"en","type":"article","venue":"","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Microgrid; State of charge; Renewable energy; Computer science; Reinforcement learning; Transformer; Battery (electricity); Grid; Automotive engineering; Photovoltaic system; Energy storage; Load management; Voltage; Electrical engineering; Engineering; Power (physics)","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":[0.000461973,0.0005493333,0.000610709,0.0002363194,0.0005794706,0.0007472838,0.0009569283,0.0005884612,0.002953975],"category_scores_gemma":[0.0005274334,0.0002201118,0.0002594238,0.0002024739,0.0003385609,0.0004498419,0.000646768,0.0005916973,0.0005383788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005997001,"about_ca_system_score_gemma":0.0007206979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00435448,"about_ca_topic_score_gemma":0.004511499,"domain_scores_codex":[0.9997862,0.00005249404,0.00001679177,0.00005613315,0.00005790253,0.00003045229],"domain_scores_gemma":[0.9997841,0.00004719088,0.00004485211,0.00001470188,0.00008334544,0.00002591789],"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":[0.0002559239,0.0002352794,0.001354145,0.0001856657,0.00007320158,0.0003879278,0.000138106,0.8756941,0.01208049,0.00391449,0.002726379,0.1029544],"study_design_scores_gemma":[0.00002250008,0.000082897,0.0001586622,0.000005865032,0.000009395884,0.00002741802,0.00000770738,0.9981083,0.0006097938,0.0004538878,0.0005069144,0.000006607414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1157181,0.0005274282,0.8645787,0.0006636838,0.000192539,0.0003426859,0.0001193718,0.002131578,0.01572605],"genre_scores_gemma":[0.9791179,0.00008237162,0.01782065,0.00006934937,0.00002426044,0.000126272,0.00003961361,0.00001262645,0.00270699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00435448,"threshold_uncertainty_score":0.009882033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00328765115988142,"score_gpt":0.1524845118620274,"score_spread":0.149196860702146,"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."}}