{"id":"W4206769499","doi":"10.1109/icjece.2021.3123091","title":"Stochastic Optimal Power Flow in Hybrid Power System Using Reduced-Discrete Point Estimation Method and Latin Hypercube Sampling","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Latin hypercube sampling; Monte Carlo method; Mathematical optimization; Probabilistic logic; Random variable; Intermittency; Computer science; Probability distribution; Point estimation; Sampling (signal processing); Cumulative distribution function; Wind speed; Mathematics; Algorithm; Probability density function; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001436332,0.0006057459,0.0007404086,0.0006405364,0.000370719,0.0006642273,0.0005619507,0.0005203159,0.001758832],"category_scores_gemma":[0.002622022,0.0003472567,0.0006088553,0.0007535262,0.0005562807,0.0006438871,0.0005534015,0.0006432603,0.0001579005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006058248,"about_ca_system_score_gemma":0.0009400317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00744296,"about_ca_topic_score_gemma":0.004270202,"domain_scores_codex":[0.9993829,0.0004151289,0.00001762478,0.00005664553,0.00009459159,0.00003309122],"domain_scores_gemma":[0.9986764,0.0009779671,0.00009328939,0.00004286144,0.0001805557,0.00002887422],"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":[0.00003036083,0.00001583523,0.0002877063,0.0000340334,0.00001382786,0.00002568535,0.00002424238,0.9862075,0.0003592393,0.003916649,0.0001754761,0.008909416],"study_design_scores_gemma":[0.000002230144,0.000005541606,0.0000346472,0.000001266913,7.908578e-7,0.000002306376,0.000002732909,0.9991083,0.00007312217,0.0007107126,0.00005690656,0.000001470944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01926818,0.0001398132,0.9788932,0.00008543758,0.00001495942,0.00005204363,0.00003882761,0.0001041973,0.00140328],"genre_scores_gemma":[0.6202,0.0003042297,0.3769607,0.00005741592,0.00003627443,0.0004252127,0.0002473279,0.00008015947,0.001688652],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00744296,"threshold_uncertainty_score":0.01479924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01009634949422904,"score_gpt":0.2045074945082645,"score_spread":0.1944111450140354,"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."}}