| 1 |
Image recognition of wind turbine blade damage based on a deep learning model with transfer learning and an ensemble learning classifier
Yang XY, Zhang YF, Lv W, Wang D
Renewable Energy, 163, 386, 2021 |
| 2 |
A subspace ensemble regression model based slow feature for soft sensing application
Jia Q, Cai J, Jiang XY, Li SJ
Chinese Journal of Chemical Engineering, 28(12), 3061, 2020 |
| 3 |
Data science-based modeling of the lysine fermentation process
Tokuyama K, Shimodaira Y, Terawaki T, Kusunose Y, Nakai H, Tsuji Y, Toya Y, Matsuda F, Shimizu H
Journal of Bioscience and Bioengineering, 130(4), 409, 2020 |
| 4 |
A hybrid wind power forecasting approach based on Bayesian model averaging and ensemble learning
Wang G, Jia R, Liu JH, Zhang HG
Renewable Energy, 145, 2426, 2020 |
| 5 |
Line-line fault detection and classification for photovoltaic systems using ensemble learning model based on I-V characteristics
Eskandari A, Milimonfared J, Aghaei M
Solar Energy, 211, 354, 2020 |
| 6 |
Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods
Liu H, Chen C, Lv XW, Wu X, Liu M
Energy Conversion and Management, 195, 328, 2019 |
| 7 |
A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems
Cheng LF, Yu T
International Journal of Energy Research, 43(6), 1928, 2019 |
| 8 |
Capreomycin resistance prediction in two species of Mycobacterium using a stacked ensemble method
Chowdhury AS, Khaledian E, Broschat SL
Journal of Applied Microbiology, 127(6), 1656, 2019 |
| 9 |
Robust ensemble learning framework for day-ahead forecasting of household based energy consumption
Alobaidi MH, Chebana F, Meguid MA
Applied Energy, 212, 997, 2018 |
| 10 |
A prediction and outlier detection scheme of molten steel temperature in ladle furnace
Wang B, Mao ZZ, Huang KK
Chemical Engineering Research & Design, 138, 229, 2018 |
