| 1 |
Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy
Li YF, Shi HP, Han FZ, Duan Z, Liu H
Renewable Energy, 135, 540, 2019 |
| 2 |
Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment
Ahmad T, Chen HX
Energy, 160, 1008, 2018 |
| 3 |
A hybrid model based on selective ensemble for energy consumption forecasting in China
Xiao J, Li YX, Xie L, Liu DH, Huang J
Energy, 159, 534, 2018 |
| 4 |
Effective sparse adaboost method with ESN and FOA for industrial electricity consumption forecasting in China
Wang L, Lv SX, Zeng YR
Energy, 155, 1013, 2018 |
| 5 |
Big multi-step wind speed forecasting model based on secondary decomposition, ensemble method and error correction algorithm
Liu H, Duan Z, Han FZ, Li YF
Energy Conversion and Management, 156, 525, 2018 |
| 6 |
Multi-step ahead wind speed forecasting using a hybrid model based on two-stage decomposition technique and AdaBoost-extreme learning machine
Peng T, Zhou JZ, Zhang C, Zheng Y
Energy Conversion and Management, 153, 589, 2017 |
| 7 |
Calibration of soft sensor by using Just-in-time modeling and AdaBoost learning method
Min H, Luo XL
Chinese Journal of Chemical Engineering, 24(8), 1038, 2016 |
| 8 |
Comparison of four Adaboost algorithm based artificial neural networks in wind speed predictions
Liu H, Tian HQ, Li YF, Zhang L
Energy Conversion and Management, 92, 67, 2015 |
| 9 |
Learning decision rules for energy efficient building control
Domahidi A, Ullmann F, Morari M, Jones CN
Journal of Process Control, 24(6), 763, 2014 |
| 10 |
Automated apple stem end and calyx detection using evolution-constructed features
Zhang D, Lillywhite KD, Lee DJ, Tippetts BJ
Journal of Food Engineering, 119(3), 411, 2013 |
