| 1 |
SEM-REV offshore energy site wind-wave bivariate statistics by hindcast
Gaidai O, Xu XS, Wang JL, Ye RC, Cheng Y, Karpa O
Renewable Energy, 156, 689, 2020 |
| 2 |
Theoretical energy storage system sizing method and performance analysis for wind power forecast uncertainty management
Oh E, Son SY
Renewable Energy, 155, 1060, 2020 |
| 3 |
Conservatively Perturbed Equilibrium (CPE) in chemical kinetics
Yablonsky GS, Branco PD, Marin GB, Constales D
Chemical Engineering Science, 196, 384, 2019 |
| 4 |
A peak-over-threshold search method for global optimization
Gao SY, Shi LY, Zhang ZJ
Automatica, 89, 83, 2018 |
| 5 |
Anomaly detection and fault analysis of wind turbine components based on deep learning network
Zhao HS, Liu HH, Hu WJ, Yan XH
Renewable Energy, 127, 825, 2018 |
| 6 |
Potential contributions of wind power to a stable and highly renewable Swiss power supply
Kruyt B, Lehning M, Kahl A
Applied Energy, 192, 1, 2017 |
| 7 |
Modelling non-stationary time series using a peaks over threshold distribution with time varying covariates and threshold: An application to peak electricity demand
Sigauke C, Bere A
Energy, 119, 152, 2017 |
| 8 |
Introducing a system of wind speed distributions for modeling properties of wind speed regimes around the world
Jung C, Schindler D, Laible J, Buchholz A
Energy Conversion and Management, 144, 181, 2017 |
| 9 |
SEM-REV energy site extreme wave prediction
Gaidai O, Ji CY, Kalogeri C, Gao JL
Renewable Energy, 101, 894, 2017 |
| 10 |
Extreme global solar irradiance due to cloud enhancement in northeastern Brazil
de Andrade RC, Tiba C
Renewable Energy, 86, 1433, 2016 |
