{"id":"W4391422956","doi":"10.1109/ias54024.2023.10406423","title":"A Model-Free Multi-Agent Reinforcement Learning Approach to Reach a Robust, Optimal, and Environment-Friendly Power Management in a Micro-Grid","year":2023,"lang":"en","type":"article","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Reinforcement learning; Photovoltaic system; Computer science; Artificial neural network; Renewable energy; Turbine; Grid; Hyperparameter; Wind power; Hyperparameter optimization; Environmentally friendly; Artificial intelligence; Engineering; Electrical engineering; Support vector machine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004126255,0.0003631859,0.00026807,0.0005862392,0.00007840958,0.00007637423,0.0003895464,0.00007581213,0.00005576206],"category_scores_gemma":[0.00001029507,0.0003835392,0.00006029432,0.0004668375,0.00002563073,0.0001490811,0.001046101,0.0002236979,0.0001824713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003125157,"about_ca_system_score_gemma":0.000004068236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003656129,"about_ca_topic_score_gemma":0.000008704943,"domain_scores_codex":[0.9978731,0.00002707724,0.0004219479,0.000588352,0.0003696695,0.0007198218],"domain_scores_gemma":[0.9990878,0.00001394637,0.00002858387,0.0006750078,0.000007475943,0.0001871965],"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":[0.00001455084,0.00005970738,0.00009750303,0.00008962617,0.0001185595,0.00003107508,0.0007377767,0.9825763,0.0004443106,0.0007445982,0.01454966,0.00053633],"study_design_scores_gemma":[0.001192497,0.00005465275,0.001075277,0.00002600111,0.00002536186,0.000002561321,0.0009206429,0.9740032,0.000144668,0.00000811576,0.02211476,0.0004322394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.110268,0.0001208311,0.8241438,0.0002134114,0.0002439537,0.001342968,0.000004292597,0.00108536,0.06257737],"genre_scores_gemma":[0.5299498,0.001205525,0.4415222,0.0002726698,0.0000733091,0.001473194,0.0001171516,0.0002367094,0.02514952],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4196818,"threshold_uncertainty_score":0.9998617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02063406003747383,"score_gpt":0.1999821156698802,"score_spread":0.1793480556324064,"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."}}