{"id":"W2344925178","doi":"10.3390/app6050122","title":"An Enhanced System Architecture for Optimized Demand Side Management in Smart Grid","year":2016,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; University of Alberta","funders":"","keywords":"Computer science; Smart grid; Knapsack problem; Demand response; Carbon footprint; Scheduling (production processes); Architecture; MATLAB; Grid; Distributed computing; Embedded system; Real-time computing; Electricity; Engineering; Operating system; Greenhouse gas; Algorithm; Operations management","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":[0.0001952027,0.000337986,0.0003635251,0.0001528702,0.0003053539,0.0007709882,0.0006763867,0.0004057144,0.003198098],"category_scores_gemma":[0.0001813768,0.0001556498,0.0002805424,0.0001554275,0.0002073455,0.0007728033,0.0004222343,0.0004423283,0.0007426473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000453702,"about_ca_system_score_gemma":0.000642971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002506308,"about_ca_topic_score_gemma":0.003248611,"domain_scores_codex":[0.9998707,0.00002917887,0.000007726278,0.00003422166,0.00003985905,0.00001830999],"domain_scores_gemma":[0.9999192,0.00001231182,0.000008693849,0.00001922232,0.00003174324,0.000008870318],"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":[0.0002777098,0.0002162838,0.001864644,0.0001662702,0.00007517232,0.0002790327,0.0001504931,0.7974932,0.05434406,0.02311616,0.004971538,0.1170453],"study_design_scores_gemma":[0.00001908942,0.0000858724,0.0003306037,0.000005286533,0.00001582955,0.00003547375,0.00001234709,0.9871373,0.006214039,0.002231086,0.00390451,0.000008486989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1111046,0.0002921037,0.8655866,0.0003636773,0.0001272629,0.0001755492,0.0002436164,0.006728307,0.01537835],"genre_scores_gemma":[0.9229262,0.0001297945,0.07132609,0.00007144726,0.00003176003,0.00008697126,0.0001825333,0.00006490048,0.005180344],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003198098,"threshold_uncertainty_score":0.01069868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008204597729595357,"score_gpt":0.2133013636004495,"score_spread":0.2050967658708541,"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."}}