{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000524368,0.0001601462,0.0001774142,0.0002068491,0.00009402879,0.0000537717,0.0004183447,0.00003726819,0.000008850625],"category_scores_gemma":[0.000003752866,0.0001101571,0.00003240862,0.0002987164,0.00008718259,0.000105818,0.00004772078,0.00003606504,0.00001980696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008843376,"about_ca_system_score_gemma":0.000006397836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005511221,"about_ca_topic_score_gemma":0.00005485443,"domain_scores_codex":[0.9987807,0.00001464248,0.0002200109,0.000364403,0.0002216053,0.0003986931],"domain_scores_gemma":[0.9995602,0.00007946316,0.0000286387,0.0002576863,0.000007803409,0.00006618461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003154478,0.00001797524,0.00002992294,0.0001242585,0.00003490008,0.000003584091,0.000136333,0.9282951,0.02445269,0.02434269,0.0004731065,0.0220579],"study_design_scores_gemma":[0.01777946,0.0006153255,0.01056348,0.001045534,0.0002227511,0.00001402054,0.005940238,0.3989448,0.4943639,0.01225618,0.05418883,0.004065569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.17809,0.00005121023,0.7451268,0.0001488814,0.0009445093,0.0009901721,0.00000594036,0.0005425695,0.07409988],"genre_scores_gemma":[0.9678804,0.00001660223,0.03131465,0.00004293386,0.0001144951,0.0005421034,0.000002207373,0.00001802255,0.00006856525],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7897904,"threshold_uncertainty_score":0.4492078,"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."}}