{"id":"W3033589589","doi":"10.1016/j.scs.2020.102285","title":"Performance of a self-learning predictive controller for peak shifting in a building integrated with energy storage","year":2020,"lang":"en","type":"article","venue":"Sustainable Cities and Society","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hydro-Québec; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"TRNSYS; Thermal energy storage; Load shifting; Peak demand; Environmental science; Electricity; Demand response; Computer science; Automotive engineering; Controller (irrigation); Energy storage; Electricity pricing; Heating system; Simulation; Thermal; Electricity market; Engineering; Meteorology; Electrical engineering; Mechanical engineering; Power (physics)","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":[0.0002536301,0.0004168428,0.0004155467,0.0002096815,0.0004918555,0.0005007273,0.0004447005,0.0005122339,0.00176037],"category_scores_gemma":[0.0004428209,0.0001776884,0.0001732379,0.0001432267,0.000267871,0.0002229363,0.0003352709,0.000415161,0.0001863656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003394514,"about_ca_system_score_gemma":0.000439813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00838291,"about_ca_topic_score_gemma":0.006081011,"domain_scores_codex":[0.999881,0.00001682783,0.000007164314,0.00002812123,0.00003721602,0.00002965739],"domain_scores_gemma":[0.9997591,0.00009325721,0.00002561929,0.00001896962,0.00008392721,0.00001914895],"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":[0.001738218,0.0004614476,0.003035138,0.0003907607,0.0001642227,0.0003491602,0.0002177115,0.8161948,0.09120398,0.001172641,0.001578111,0.08349381],"study_design_scores_gemma":[0.00002672715,0.0002363203,0.001448437,0.000006031733,0.0000230344,0.00002310981,0.00002527967,0.9850304,0.01283545,0.0001001667,0.0002378399,0.000007385014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8957906,0.0002940155,0.09130699,0.0002933198,0.0001534128,0.00006121508,0.00006454567,0.0009925889,0.01104338],"genre_scores_gemma":[0.9986099,0.00001249523,0.0008754235,0.00001010926,0.000002557074,0.000005302835,0.000007093914,0.000004380247,0.000472834],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00838291,"threshold_uncertainty_score":0.01666826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002785421375309326,"score_gpt":0.159642851342808,"score_spread":0.1568574299674987,"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."}}