{"id":"W3144445876","doi":"10.1049/rpg2.12169","title":"Risk‐adjustable stochastic schedule based on Sobol augmented Latin hypercube sampling considering correlation of wind power uncertainties","year":2021,"lang":"en","type":"article","venue":"IET Renewable Power Generation","topic":"Electric Power System Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Latin hypercube sampling; Sobol sequence; Schedule; Computer science; Sampling (signal processing); Correlation; Mathematical optimization; Mathematics; Applied mathematics; Monte Carlo method; Statistics","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":[0.000352646,0.0002918741,0.0003517636,0.0002607993,0.0001795304,0.0001073552,0.00009313598,0.0002012653,0.0002893714],"category_scores_gemma":[0.0003545626,0.0003361339,0.00008798931,0.0006287023,0.00002756335,0.0002884386,0.00002221953,0.00020245,0.00001828145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002437237,"about_ca_system_score_gemma":0.0001992098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001169433,"about_ca_topic_score_gemma":0.0001487663,"domain_scores_codex":[0.9980724,0.000135913,0.0006153819,0.0003856869,0.0004239282,0.0003666622],"domain_scores_gemma":[0.9987312,0.000181525,0.0001987415,0.0004488392,0.0003542689,0.00008540263],"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":[0.00001744546,0.00003754525,0.000514586,0.0000351698,0.0000528435,0.000002300495,0.0002522628,0.8949168,0.1032757,0.0001147333,0.000670149,0.0001104785],"study_design_scores_gemma":[0.0007048574,0.00009136609,0.0001968661,0.0001284267,0.00005380359,0.0000051342,0.0001832944,0.8855459,0.1125141,0.00004942895,0.0002374923,0.0002893458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1971637,0.0006156335,0.79814,0.00004357285,0.00118233,0.0003334451,0.00003243544,0.0002277007,0.002261154],"genre_scores_gemma":[0.9855413,0.00002773221,0.01354722,0.00004718976,0.0000840651,0.00002512856,0.0002898413,0.00007436119,0.0003631157],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7883777,"threshold_uncertainty_score":0.999909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0177319018927179,"score_gpt":0.2191802519348508,"score_spread":0.2014483500421329,"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."}}