| 1 |
A new wind speed scenario generation method based on spatiotemporal dependency structure
Deng JC, Li HR, Hu JX, Liu ZY
Renewable Energy, 163, 1951, 2021 |
| 2 |
Process monitoring method based on correlation variable classification and vine copula
Cui Q, Li SJ
Canadian Journal of Chemical Engineering, 98(6), 1411, 2020 |
| 3 |
Long-term assessment of a floating offshore wind turbine under environmental conditions with multivariate dependence structures
Li X, Zhang W
Renewable Energy, 147, 764, 2020 |
| 4 |
Long-term fatigue damage assessment for a floating offshore wind turbine under realistic environmental conditions
Li X, Zhang W
Renewable Energy, 159, 570, 2020 |
| 5 |
Fault detection of wind turbines via multivariate process monitoring based on vine copulas
Xu QF, Fan ZH, Jia WY, Jiang CX
Renewable Energy, 161, 939, 2020 |
| 6 |
A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data
Niemierko R, Toppel J, Trankler T
Applied Energy, 233, 691, 2019 |
| 7 |
Predicting the frequency of abnormal events in chemical process with Bayesian theory and vine copula
Lv C, Zhang ZY, Ren X, Li SJ
Journal of Loss Prevention in The Process Industries, 32, 192, 2014 |
| 8 |
Spatial dependence in wind and optimal wind power allocation: A copula-based analysis
Grothe O, Schnieders J
Energy Policy, 39(9), 4742, 2011 |
