{"id":"W4402475573","doi":"10.1109/ccece59415.2024.10667207","title":"A Reinforcement Learning Controller Based on Double DQN for DC microgrids with Constant Power Loads","year":2024,"lang":"en","type":"article","venue":"","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Reinforcement learning; Control theory (sociology); Constant (computer programming); Computer science; Controller (irrigation); Power (physics); Control (management); Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001278582,0.0001829285,0.0001823849,0.0001134624,0.00006006608,0.0001490384,0.00005883695,0.00006174668,0.0004209903],"category_scores_gemma":[0.000004489972,0.0001327069,0.00008158326,0.0001307625,0.00001955085,0.00008035345,0.000006220966,0.0001359727,0.00004821195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007142106,"about_ca_system_score_gemma":0.00003543158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006673073,"about_ca_topic_score_gemma":0.00000493843,"domain_scores_codex":[0.999211,0.000006649588,0.0001951511,0.000191352,0.0001213088,0.0002745204],"domain_scores_gemma":[0.9996881,0.00007684335,0.00001512672,0.0001061736,0.00005354431,0.00006019986],"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":[0.0004288176,0.00001177707,0.00001770211,0.00007324375,0.0001033551,0.000005592066,0.0000665187,0.9889384,0.003427887,0.002086364,0.002684176,0.002156155],"study_design_scores_gemma":[0.003444097,0.0002932826,0.00000331119,0.0000953971,0.00003806914,0.000002634817,0.00003744195,0.905458,0.003550464,0.000008009492,0.08687488,0.0001944179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002139324,0.001438219,0.9498712,0.0003749431,0.0003836435,0.001074585,0.000008390788,0.001109052,0.04360063],"genre_scores_gemma":[0.9947289,0.00003579243,0.00253282,0.0002634019,0.00006102866,0.0001490309,0.00003598419,0.00005085975,0.002142241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9925895,"threshold_uncertainty_score":0.5411631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004706244080884359,"score_gpt":0.1928691020548759,"score_spread":0.1881628579739915,"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."}}