{"id":"W4280642238","doi":"10.3389/fenrg.2022.861571","title":"Optimal Scheduling of Demand Side Load Management of Smart Grid Considering Energy Efficiency","year":2022,"lang":"en","type":"article","venue":"Frontiers in Energy Research","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Smart grid; Demand response; Computer science; Energy management; Grid; Scheduling (production processes); Load management; Demand side; Distributed computing; Load balancing (electrical power); Electricity; Engineering; Energy (signal processing); Operations management; Environmental economics; Electrical engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00165288,0.0002141949,0.0004173701,0.001014609,0.0001533359,0.00001932108,0.0006895139,0.00006342716,0.00007966036],"category_scores_gemma":[0.00003094185,0.0002597713,0.0001003626,0.001473452,0.000201423,0.0001009697,0.0009220291,0.0003619199,9.287378e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008390193,"about_ca_system_score_gemma":0.0000750458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006827165,"about_ca_topic_score_gemma":0.00006243265,"domain_scores_codex":[0.9966133,0.0002446085,0.0006080084,0.0004069932,0.001360086,0.0007670643],"domain_scores_gemma":[0.9990729,0.00008266035,0.00006506946,0.0005752603,0.0001041471,0.00009996078],"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":[0.00007906767,0.000106463,0.00124377,0.000210849,0.0002109513,0.0001189946,0.0001454341,0.9815368,0.0006956192,0.007119028,0.004190171,0.004342831],"study_design_scores_gemma":[0.001433649,0.000243313,0.0007351324,0.0001676551,0.00003107659,0.000008854718,0.004779826,0.899724,0.02958984,0.000728417,0.06209837,0.0004598848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5745071,0.01611841,0.340274,0.000126039,0.00532359,0.0003852369,0.00002934302,0.0002717245,0.06296449],"genre_scores_gemma":[0.9770949,0.00129,0.02062713,0.00001201896,0.00007284384,0.0002233237,0.0000129888,0.00006635488,0.0006004167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4025878,"threshold_uncertainty_score":0.9999855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0198710179254533,"score_gpt":0.2541826947727595,"score_spread":0.2343116768473062,"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."}}