| 1 |
Day-ahead renewable scenario forecasts based on generative adversarial networks
Jiang CM, Mao YF, Chai Y, Yu MB
International Journal of Energy Research, 45(5), 7572, 2021 |
| 2 |
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 |
| 3 |
Fuzzy distributional chance-constrained programming for handling stochastic and epistemic uncertainties during flotation processes
Liang Y, He DK, Wang QK, Lu XL
Chemical Engineering Research & Design, 164, 248, 2020 |
| 4 |
Stochastic optimization based on a novel scenario generation method for midstream and downstream petrochemical supply chain
Zang PX, Sun GM, Zhao YM, Luo YQ, Yuan XG
Chinese Journal of Chemical Engineering, 28(3), 815, 2020 |
| 5 |
Generating linked technology-socioeconomic scenarios for emerging energy transitions
Small MJ, Wong-Parodi G, Kefford BM, Stringer M, Schmeda-Lopez DR, Greig C, Ballinger B, Wilson S, Smart S
Applied Energy, 239, 1402, 2019 |
| 6 |
Uncertainty quantification and scenario generation of future solar photovoltaic price for use in energy system models
Kim H, Cheon H, Ahn YH, Choi DG
Energy, 168, 370, 2019 |
| 7 |
Risk-aware stochastic bidding strategy of renewable micro-grids in day-ahead and real-time markets
Fazlalipour P, Ehsan M, Mohammadi-Ivatloo B
Energy, 171, 689, 2019 |
| 8 |
A scenario generation method based on the mixture vine copula and its application in the power system with wind/hydrogen production
Qiu YB, Li Q, Pan YR, Yang HQ, Chen WR
International Journal of Hydrogen Energy, 44(11), 5162, 2019 |
| 9 |
A conditional model of wind power forecast errors and its application in scenario generation
Wang ZW, Shen C, Liu F
Applied Energy, 212, 771, 2018 |
| 10 |
Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach
Mavromatidis G, Orehounig K, Carmeliet J
Applied Energy, 222, 932, 2018 |
