| 1 |
ELCC-based capacity credit estimation accounting for uncertainties in capacity factors and its application to solar power in Korea
Paik C, Chung Y, Kim YJ
Renewable Energy, 164, 833, 2021 |
| 2 |
Making sense of parameter estimation and model simulation in bioprocesses
Sadino-Riquelme MC, Rivas J, Jeison D, Hayes RE, Donoso-Bravo A
Biotechnology and Bioengineering, 117(5), 1357, 2020 |
| 3 |
Effects of stripy surfaces with intervals on the coalescence dynamics of nanodroplets: Insights from molecular dynamics simulations
Li T, Xia YJ, Zhang LS, Zhang XF, Fu CR, Jiang YY, Li H
Applied Surface Science, 481, 951, 2019 |
| 4 |
Modeling and Parameter Identification foraBiofilm in a Microbial Fuel Cell
Kubannek F, Krewer U
Chemie Ingenieur Technik, 91(6), 856, 2019 |
| 5 |
Determining the number of segments for piece-wise linear representation of discrete-time signals
Wang JD, Yu Y, Chen K
Computers & Chemical Engineering, 120, 46, 2019 |
| 6 |
Frequency-domain estimates of the sampling interval in multirate nonlinear systems by time-delay approach
Bryntseva TA, Fradkov AL
International Journal of Control, 92(9), 1985, 2019 |
| 7 |
A multi-objective wind speed and wind power prediction interval forecasting using variational modes decomposition based Multi-kernel robust ridge regression
Naik J, Dash PK, Dhar S
Renewable Energy, 136, 701, 2019 |
| 8 |
A novel prediction intervals method integrating an error & self-feedback extreme learning machine with particle swarm optimization for energy consumption robust prediction
Xu Y, Zhang MQ, Ye LL, Zhu QX, Geng ZQ, He YL, Han YM
Energy, 164, 137, 2018 |
| 9 |
Analysis of spring operated pressure relief valve proof test data: Findings and implications
Bukowski JV, Goble WM, Gross RE, Harris SP
Process Safety Progress, 37(4), 467, 2018 |
| 10 |
An advanced approach for optimal wind power generation prediction intervals by using self-adaptive evolutionary extreme learning machine
Mahmoud T, Dong ZY, Ma J
Renewable Energy, 126, 254, 2018 |
