{"id":"W3173438469","doi":"10.1109/access.2022.3156581","title":"Building Energy Management With Reinforcement Learning and Model Predictive Control: A Survey","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Model predictive control; Reinforcement learning; Computer science; Energy management; Building automation; Building management system; Efficient energy use; Renewable energy; Automation; Control (management); Energy (signal processing); Artificial intelligence; Engineering","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":[0.0004841602,0.001064753,0.001356202,0.001115737,0.0002479065,0.001609317,0.001089416,0.001132317,0.002498921],"category_scores_gemma":[0.0008886047,0.0004199832,0.0007606427,0.002651986,0.000459595,0.001569739,0.0008111699,0.0009831314,0.0009430939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004405794,"about_ca_system_score_gemma":0.0006078511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00171426,"about_ca_topic_score_gemma":0.00106447,"domain_scores_codex":[0.9996392,0.00006144588,0.00003870851,0.00009090955,0.0001464626,0.00002325102],"domain_scores_gemma":[0.9996063,0.0002395891,0.00003464226,0.00002849162,0.00007660577,0.00001434354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006171141,0.0001705734,0.0008627485,0.006822954,0.000127206,0.0001257158,0.0001003217,0.06879658,0.001464069,0.02826278,0.008704635,0.8845008],"study_design_scores_gemma":[0.00005397151,0.0007047283,0.003250542,0.003593996,0.0003239831,0.0009678304,0.0002919342,0.3596674,0.004363195,0.06615081,0.5604331,0.0001985816],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.004372196,0.7862816,0.184444,0.001016312,0.0005935311,0.00009040719,0.0001306374,0.0003620239,0.02270913],"genre_scores_gemma":[0.1070126,0.8418028,0.04209328,0.0004499874,0.002020251,0.0001596932,0.0003680418,0.0000906882,0.006002673],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002498921,"threshold_uncertainty_score":0.00835973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01109218519743361,"score_gpt":0.2253678702973813,"score_spread":0.2142756850999477,"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."}}