{"id":"W4360584207","doi":"10.1109/isgt51731.2023.10066398","title":"Power Management in Smart Buildings Using Reinforcement Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Reinforcement learning; Schedule; Computer science; Markov decision process; Particle swarm optimization; Mathematical optimization; Energy storage; Energy management; Metaheuristic; Battery (electricity); Microgrid; Markov process; Power (physics); Energy (signal processing); Artificial intelligence; Machine learning; Mathematics","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":[0.0006016673,0.0004296057,0.0005716488,0.0001685986,0.0002326846,0.0005041617,0.000584738,0.0004653636,0.0009700924],"category_scores_gemma":[0.001141842,0.0001841273,0.0002671635,0.0001613772,0.0005915045,0.0006002706,0.0004698679,0.000637638,0.0001442387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005384904,"about_ca_system_score_gemma":0.0005602543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003372699,"about_ca_topic_score_gemma":0.003092356,"domain_scores_codex":[0.9997281,0.00009953442,0.00001177729,0.00005806522,0.00006677112,0.00003567415],"domain_scores_gemma":[0.9996129,0.0002009502,0.00007101604,0.00002966377,0.00006083332,0.00002468902],"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":[0.00002695539,0.00004608,0.0005379636,0.00002744065,0.00002094477,0.00004121173,0.00002495821,0.9689236,0.001353008,0.004508562,0.0002669673,0.02422246],"study_design_scores_gemma":[0.000005615545,0.00001849948,0.00006377715,0.000001822546,0.000002022815,0.000005516256,0.000002860342,0.9981486,0.0001887636,0.001369744,0.0001910972,0.000001740446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03952655,0.0002846032,0.9555735,0.0002012796,0.00003764313,0.00004723119,0.00001636022,0.0003596207,0.003953162],"genre_scores_gemma":[0.9700313,0.0001253221,0.02861527,0.00005002043,0.00002308676,0.00004669968,0.00001764345,0.00001498537,0.001075692],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003372699,"threshold_uncertainty_score":0.006706119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01223427902924419,"score_gpt":0.2205747348603178,"score_spread":0.2083404558310736,"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."}}