{"id":"W2560789657","doi":"10.1109/pmaps.2016.7764136","title":"Cumulant-based probabilistic load flow analysis of wind power and electric vehicles","year":2016,"lang":"en","type":"article","venue":"","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Probabilistic logic; Monte Carlo method; Reliability (semiconductor); Wind power; Computer science; Probabilistic analysis of algorithms; Power-flow study; Cumulant; Random variable; Power (physics); Grid; Electric power system; Mathematical optimization; Reliability engineering; Probability distribution; AC power; Voltage; Engineering; Mathematics; Electrical engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.00007893005,0.0001230731,0.0002400145,0.0002142067,0.00001939805,0.00001149392,0.00007898451,0.00007506434,0.0002687823],"category_scores_gemma":[0.00003358228,0.00007663831,0.00007596691,0.0007749412,0.00001945965,0.00005907108,0.000008663373,0.00005851319,0.000004380912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005696525,"about_ca_system_score_gemma":0.00002853978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001367411,"about_ca_topic_score_gemma":0.00001785925,"domain_scores_codex":[0.9992799,0.00001195817,0.0001901695,0.0001485514,0.0001596882,0.0002097646],"domain_scores_gemma":[0.9995794,0.00009172317,0.00002517386,0.0001787009,0.00006417937,0.00006084624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005814058,0.00006194628,0.03619496,0.0001428782,0.001653184,0.000008259623,0.0001417812,0.1343714,0.6810097,0.001481746,0.002232657,0.1426435],"study_design_scores_gemma":[0.0007659741,0.0001629256,0.0997949,0.00002817401,0.0005334195,0.000003332621,0.000006814763,0.817354,0.07961664,0.0006592823,0.0007363669,0.0003381441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9849644,0.0003590779,0.01297324,0.00006392349,0.00002228247,0.00008506604,0.00001086979,0.0001050138,0.001416159],"genre_scores_gemma":[0.9987874,0.00003080602,0.001046455,0.00003353508,0.00001229728,0.000001807602,0.000001735085,0.00001404807,0.00007189871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6829827,"threshold_uncertainty_score":0.312522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004164036681440428,"score_gpt":0.1888549480154603,"score_spread":0.1846909113340198,"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."}}