{"id":"W2547310136","doi":"10.1109/ias.2016.7731970","title":"Load aggregation from generation-follows-load to load-follows-generation","year":2016,"lang":"en","type":"article","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Smart grid; Computer science; Load balancing (electrical power); Demand response; Load management; Electric power system; Grid; Load profile; Distributed computing; Electricity generation; Power (physics); Control engineering; Electrical engineering; Electricity; 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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002548709,0.0002898696,0.0002079374,0.0001001968,0.00009800831,0.0001144001,0.0002169742,0.0001218279,0.001160336],"category_scores_gemma":[0.0001143026,0.0002343297,0.00008948756,0.0002656264,0.0000149098,0.0004350363,0.00006962181,0.00006024081,0.002087695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001349665,"about_ca_system_score_gemma":0.00009099466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006916638,"about_ca_topic_score_gemma":0.004522872,"domain_scores_codex":[0.9980313,0.00003524508,0.0003976623,0.0004471872,0.0007337743,0.0003548785],"domain_scores_gemma":[0.9989635,0.00004274436,0.00003898565,0.0005914891,0.0001949484,0.0001682825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009869513,0.00002869191,0.0003930487,0.000006928925,0.0001487686,0.0000075352,0.0002522436,0.1874442,0.4633314,0.001470934,0.3039058,0.04300062],"study_design_scores_gemma":[0.001697736,0.0000782468,0.00168168,0.00006644592,0.00007678822,0.000001553643,0.00003476208,0.2076277,0.3756646,0.000230109,0.4118458,0.0009946236],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5659544,0.00031295,0.3854316,0.001454545,0.004624743,0.0004556759,0.00002534887,0.001052525,0.04068825],"genre_scores_gemma":[0.9712752,0.0001076721,0.008770976,0.0005923765,0.002639669,0.0001710676,0.00006977601,0.00007937176,0.01629397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4053208,"threshold_uncertainty_score":0.9997528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01719911927924085,"score_gpt":0.2052892076626628,"score_spread":0.1880900883834219,"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."}}