{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007129714,0.000527793,0.001299653,0.0003594773,0.0003559347,0.0007937616,0.0007397562,0.000855566,0.001843794],"category_scores_gemma":[0.001204827,0.0003579096,0.0004044508,0.000482775,0.000509281,0.0006281407,0.0005817614,0.0005737785,0.0001924674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007573864,"about_ca_system_score_gemma":0.000858031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01149916,"about_ca_topic_score_gemma":0.007500248,"domain_scores_codex":[0.9996898,0.0001098467,0.00001155264,0.00005928215,0.00005666044,0.00007296037],"domain_scores_gemma":[0.9994416,0.0003133576,0.00006080899,0.00002279217,0.0001178571,0.00004371936],"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":[0.00003220997,0.00002573085,0.0003505634,0.00001215931,0.00001280204,0.00001711958,0.00000732811,0.9943755,0.0002708556,0.0006623775,0.000173555,0.004059938],"study_design_scores_gemma":[0.000004287991,0.00000951659,0.00006368845,8.723094e-7,0.000001946682,0.000001761191,0.000002244124,0.9996561,0.00004268384,0.0001845526,0.00003152199,9.374567e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2334426,0.0005932006,0.7549648,0.0004241668,0.00008744941,0.00008651528,0.00008817505,0.0004646753,0.009848362],"genre_scores_gemma":[0.9868783,0.00005597365,0.01160136,0.0000278267,0.000008356421,0.00003015675,0.00003131428,0.00001334538,0.001353445],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01149916,"threshold_uncertainty_score":0.02286446,"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."}}