{"id":"W4320039231","doi":"10.1016/j.enbuild.2023.112878","title":"Mitigating an adoption barrier of reinforcement learning-based control strategies in buildings","year":2023,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Alberta Machine Intelligence Institute","keywords":"Reinforcement learning; Control (management); Set (abstract data type); Computer science; Population; Cluster analysis; Training (meteorology); Engineering; Architectural engineering; Machine learning; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002444949,0.0001507288,0.0001839505,0.0002580703,0.0000803373,0.00004305536,0.00008779025,0.0001226477,0.0000206869],"category_scores_gemma":[0.00002028352,0.000159884,0.00003621639,0.0004005959,0.00004519703,0.0003388899,0.00001272156,0.0001238481,3.081792e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002705894,"about_ca_system_score_gemma":0.00002122258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000131646,"about_ca_topic_score_gemma":0.00003437452,"domain_scores_codex":[0.9991434,0.00002799285,0.0002740948,0.0001755768,0.0001393325,0.0002396553],"domain_scores_gemma":[0.999689,0.00005387874,0.00006069904,0.0001020312,0.00003279216,0.00006155873],"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":[0.00001885794,0.000006897033,0.001090575,0.00004183383,0.00001325096,0.000002581054,0.0001487302,0.9631638,0.01651238,0.01645431,0.00002714034,0.002519649],"study_design_scores_gemma":[0.0006895389,0.00009867755,0.0005921533,0.00009288239,0.00001095678,0.000001376364,0.0002533189,0.9743152,0.02154229,0.0005911596,0.001607567,0.0002048999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8891355,0.0001199871,0.109516,0.00002948618,0.0001085181,0.00004855133,0.000001627667,0.0003775826,0.0006627876],"genre_scores_gemma":[0.9985509,0.0001147656,0.001066237,0.00004795529,0.0000413815,0.00002682216,0.00004648444,0.00002798344,0.00007747918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1094154,"threshold_uncertainty_score":0.6519881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005628482034530541,"score_gpt":0.2013507305439212,"score_spread":0.1957222485093907,"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."}}