{"id":"W3134120822","doi":"10.3390/en14051387","title":"A Methodology for the Enhancement of the Energy Flexibility and Contingency Response of a Building through Predictive Control of Passive and Active Storage","year":2021,"lang":"en","type":"article","venue":"Energies","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Concordia University","funders":"","keywords":"Setpoint; Thermal energy storage; Flexibility (engineering); Demand response; Peak demand; Thermal mass; Energy storage; Computer science; Load shifting; Smart grid; Automotive engineering; Reliability engineering; Energy management; Model predictive control; Simulation; Engineering; Energy (signal processing); Thermal; Electricity; Control (management); Electrical engineering; Power (physics)","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":[0.0005678572,0.0008717751,0.0004925308,0.0004690494,0.0002935111,0.0007794808,0.0008765018,0.0004915522,0.002513493],"category_scores_gemma":[0.0006442128,0.0003022446,0.0006841913,0.0003481642,0.0004914039,0.0004653557,0.0004164095,0.000737235,0.0002703345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000523899,"about_ca_system_score_gemma":0.0009458263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002052458,"about_ca_topic_score_gemma":0.001960407,"domain_scores_codex":[0.9997621,0.00004875909,0.00001357241,0.0000510094,0.0001032942,0.00002123401],"domain_scores_gemma":[0.9998048,0.00007463045,0.00003973445,0.00002266025,0.00005050833,0.000007670363],"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":[0.00003646936,0.00007216871,0.0003210134,0.000330851,0.00005086412,0.0001117281,0.00007041683,0.8128306,0.02281114,0.04761541,0.0005988022,0.1151506],"study_design_scores_gemma":[0.000008661847,0.0001292588,0.0001513116,0.00002422316,0.00001892725,0.00005115003,0.000009582599,0.986652,0.004926982,0.005519408,0.002496064,0.00001247266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001490656,0.00007782651,0.9966381,0.0000211997,0.00001351672,0.00006013325,0.00001734009,0.000145826,0.001535482],"genre_scores_gemma":[0.4804441,0.0004460021,0.5150899,0.00003927954,0.00003628446,0.0006655331,0.0001029988,0.00005468752,0.003121256],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002513493,"threshold_uncertainty_score":0.008408487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02492441123317994,"score_gpt":0.2672167088110288,"score_spread":0.2422922975778488,"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."}}