{"id":"W2768071282","doi":"10.1109/ias.2017.8101862","title":"Distributed energy storage unit-based active demand response for residential loads","year":2017,"lang":"en","type":"article","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Demand response; Smart grid; Peak demand; Power demand; Computer science; Offset (computer science); Energy storage; Grid; Dynamic demand; Distributed generation; Power consumption; Power (physics); Automotive engineering; Reliability engineering; Electrical engineering; Electricity; Engineering; Renewable energy; Mathematics; Operating system","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":[0.0003002889,0.0003404993,0.0003846512,0.0002714699,0.000198867,0.0003603265,0.0009855358,0.0002810615,0.002680802],"category_scores_gemma":[0.0005839064,0.0001767623,0.00015481,0.0002331645,0.0001590702,0.0004740228,0.0002885239,0.0002578077,0.0007754523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003479056,"about_ca_system_score_gemma":0.0002291284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001239321,"about_ca_topic_score_gemma":0.002137825,"domain_scores_codex":[0.9997101,0.00008064473,0.0000183941,0.00006353143,0.0001038071,0.00002354428],"domain_scores_gemma":[0.9996389,0.0001039022,0.0000419525,0.00006798453,0.0001244707,0.00002265729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003341266,0.00159095,0.009582546,0.0007411812,0.0001536592,0.0006318163,0.0002975819,0.3138775,0.285081,0.002711558,0.009694947,0.3722959],"study_design_scores_gemma":[0.0001341227,0.0007947233,0.002875127,0.00001152903,0.00003558755,0.0001508532,0.00005653554,0.8970395,0.09373818,0.0005372118,0.004596164,0.00003048572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6349678,0.0003655968,0.3313903,0.0003321901,0.0002066015,0.0003886465,0.0007000046,0.01584441,0.0158045],"genre_scores_gemma":[0.9857858,0.00002914138,0.01227631,0.00003780595,0.000007513159,0.00003921898,0.0001326445,0.00004575536,0.001645829],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002680802,"threshold_uncertainty_score":0.008968174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01432565449275926,"score_gpt":0.2394937588801967,"score_spread":0.2251681043874375,"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."}}