{"id":"W4411943015","doi":"10.1080/19401493.2025.2524379","title":"Development of a contextual bandits-based thermal mass preconditioning algorithm for dynamic electricity pricing","year":2025,"lang":"en","type":"article","venue":"Journal of Building Performance Simulation","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Thermal; Dynamic pricing; Electricity; Computer science; Mathematical optimization; Algorithm; Environmental science; Meteorology; Economics; Mathematics; Engineering; Physics; Microeconomics; Electrical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003744322,0.0005503724,0.0004619508,0.0002363665,0.0003382025,0.0005532704,0.0007305013,0.0005635781,0.003680112],"category_scores_gemma":[0.001626877,0.0002737311,0.0003730971,0.0002093151,0.0003740336,0.0004805193,0.0007098097,0.0008737889,0.0005630653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004949057,"about_ca_system_score_gemma":0.001208258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007570912,"about_ca_topic_score_gemma":0.0080862,"domain_scores_codex":[0.999819,0.00004233192,0.00001079528,0.000035872,0.00005799656,0.00003397325],"domain_scores_gemma":[0.9996867,0.00012648,0.00003864436,0.0000349203,0.00009046776,0.00002282833],"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":[0.00006715002,0.0000360214,0.000602423,0.00002378831,0.00001496276,0.00003051454,0.00003959064,0.9441313,0.003644611,0.005297219,0.0006118999,0.04550053],"study_design_scores_gemma":[0.000004301179,0.000007769407,0.00003949983,0.000001896393,0.000001654265,0.000002852944,0.0000029798,0.9985698,0.0005971874,0.0004965836,0.0002737011,0.000001636003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03080584,0.00007725246,0.9644991,0.00008560957,0.00003906468,0.00005546887,0.00004052913,0.0007710544,0.003626213],"genre_scores_gemma":[0.6441666,0.00008651839,0.3521816,0.00009615845,0.0000366781,0.0001712933,0.0001540537,0.0002661105,0.002840836],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007570912,"threshold_uncertainty_score":0.01505369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00840976397016027,"score_gpt":0.2526641460882754,"score_spread":0.2442543821181151,"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."}}