{"id":"W2885902739","doi":"10.1109/icc.2018.8423021","title":"Data Communication and Analytics for Smart Grid Systems","year":2018,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Smart grid; Support vector machine; Mean squared error; Energy consumption; Data mining; Cloud computing; Linear regression; Polynomial regression; Machine learning; Statistics; Engineering; Mathematics","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":[0.0001258969,0.00004110113,0.00005320629,0.00001788897,0.00004898188,0.00003373969,0.0001434729,0.00002364971,0.000006734021],"category_scores_gemma":[0.00001528986,0.00003677919,0.000005067083,0.00003631949,0.00001987667,0.0000918123,0.0000546321,0.00002463306,0.000004305159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000521088,"about_ca_system_score_gemma":0.0000023175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003787264,"about_ca_topic_score_gemma":0.0001714365,"domain_scores_codex":[0.9997545,0.000004410046,0.00008171119,0.00005968655,0.00002596744,0.00007369673],"domain_scores_gemma":[0.9995191,0.00005202879,0.000008939386,0.000375307,0.00002167292,0.00002295752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003733134,0.00006282963,0.01314392,0.001369721,0.0006513563,0.00000172914,0.001579881,0.03872484,0.003951202,0.133883,0.747732,0.05886211],"study_design_scores_gemma":[0.00007358952,0.00001079806,0.00007464435,0.0000203899,0.000008414968,0.000002262838,0.00003421674,0.777542,0.0001687973,0.00003602216,0.2219782,0.00005073859],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1505914,0.005442217,0.5243364,0.0002942783,0.00372718,0.0005849341,0.000583743,0.001326226,0.3131137],"genre_scores_gemma":[0.9914171,0.00009027951,0.007565261,0.0000192193,0.0002409713,0.000004045131,0.0001830127,0.00001278258,0.0004674018],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8408256,"threshold_uncertainty_score":0.1499812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06061986730852808,"score_gpt":0.265702780024514,"score_spread":0.2050829127159859,"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."}}