{"id":"W4408099754","doi":"10.2139/ssrn.5161963","title":"Optimization of energy consumption in smart city using reinforcement learning algorithm","year":2025,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Regional Municipality of Niagara","funders":"","keywords":"Reinforcement learning; Energy consumption; Computer science; Reinforcement; Consumption (sociology); Algorithm; Artificial intelligence; Machine learning; Mathematical optimization; Engineering; Mathematics; Electrical engineering; Structural engineering; Sociology","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":[0.00121359,0.00009515202,0.0001624769,0.000435165,0.00006963614,0.00002525788,0.0001122387,0.00007288554,0.00003315407],"category_scores_gemma":[0.00004118455,0.0001046042,0.00004412241,0.0003291235,0.00001926626,0.0001187313,0.00002435696,0.000867223,0.000001502911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001642631,"about_ca_system_score_gemma":0.0004218118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002172208,"about_ca_topic_score_gemma":0.0001727278,"domain_scores_codex":[0.9983547,0.00009431178,0.0003631173,0.00009131403,0.0002123955,0.0008841839],"domain_scores_gemma":[0.9997227,0.00003909594,0.00006651467,0.00008629169,0.00006116142,0.00002428133],"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":[0.000009250783,0.00000770831,0.01268937,0.0000262372,0.00007520498,0.000001112088,0.00003046265,0.9740391,0.0009006869,0.001881499,0.000009179937,0.0103302],"study_design_scores_gemma":[0.0004889636,0.00003995118,0.0004394633,0.0001403856,0.000009732043,0.00003975368,0.0001082221,0.9964913,0.001212497,0.0006274895,0.0003156309,0.00008658433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09242115,0.001398727,0.9047565,0.000008229727,0.0001956304,0.0000663595,2.21328e-7,0.00003476585,0.001118453],"genre_scores_gemma":[0.9971247,0.001313361,0.0009542171,0.000003942454,0.00004829056,0.000004323612,0.000003817185,0.00001506661,0.0005322861],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9047036,"threshold_uncertainty_score":0.4295422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01369284672458369,"score_gpt":0.2646140027892552,"score_spread":0.2509211560646715,"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."}}