{"id":"W2901068648","doi":"10.1109/tpwrs.2018.2882560","title":"A Data-Driven Load Fluctuation Model for Multi-Region Power Systems","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electric power system; Probabilistic logic; A priori and a posteriori; Computer science; Load management; Gaussian; Random variable; Power demand; Power (physics); Mathematics; Statistics; Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002906916,0.0003553295,0.0003635202,0.0002024341,0.000276134,0.0001537037,0.0004197227,0.0002476626,0.0000138325],"category_scores_gemma":[0.000009355202,0.0003495521,0.0001209087,0.0002356164,0.00005908709,0.0004998434,0.000002896036,0.000217192,0.0001202744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002504792,"about_ca_system_score_gemma":0.00007171444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001060034,"about_ca_topic_score_gemma":0.0001806395,"domain_scores_codex":[0.9980675,0.00004749111,0.0005651266,0.000496575,0.0003483229,0.000475011],"domain_scores_gemma":[0.9984986,0.0000821997,0.00009696044,0.000921204,0.0002483455,0.0001527234],"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":[0.00004112352,0.00006967322,0.000002674818,0.0001460109,0.0001541261,0.000002815115,0.001357089,0.9928108,0.001419465,0.0002047747,0.003609207,0.0001822511],"study_design_scores_gemma":[0.0007831371,0.0001344717,0.000002775845,0.0002878238,0.00005931921,0.00004234005,0.0001930774,0.9862404,0.0005823463,0.000003824418,0.01128452,0.000385984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00544167,0.00034516,0.9814494,0.00001340856,0.009462103,0.0007448951,0.0005252754,0.0006992538,0.001318785],"genre_scores_gemma":[0.9960268,0.00002043602,0.001521363,0.00002283729,0.00008232536,0.0002277766,0.00004280694,0.0001217662,0.001933916],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9905851,"threshold_uncertainty_score":0.9998956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0676911872754359,"score_gpt":0.2715237721997845,"score_spread":0.2038325849243486,"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."}}