| 1 |
Steam consumption prediction of a gas sweetening process with methyldiethanolamine solvent using machine learning approaches
Moghadasi M, Ozgoli HA, Farhani F
International Journal of Energy Research, 45(1), 879, 2021 |
| 2 |
Vector field-based support vector regression for building energy consumption prediction
Zhong H, Wang JJ, Jia HJ, Mu YF, Lv SL
Applied Energy, 242, 403, 2019 |
| 3 |
Modal decomposition based ensemble learning for ground source heat pump systems load forecasting
Xu CL, Chen HX, Xun WD, Zhou ZX, Liu T, Zeng YK, Ahmad T
Energy and Buildings, 194, 62, 2019 |
| 4 |
Overview of the use of artificial neural networks for energy-related applications in the building sector
Guyot D, Giraud F, Simon F, Corgier D, Marvillet C, Tremeac B
International Journal of Energy Research, 43(13), 6680, 2019 |
| 5 |
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 |
| 6 |
Sample data selection method for improving the prediction accuracy of the heating energy consumption
Yuan TH, Zhu N, Shi YF, Chang C, Yang K, Ding Y
Energy and Buildings, 158, 234, 2018 |
| 7 |
A relevant data selection method for energy consumption prediction of low energy building based on support vector machine
Paudel S, Elmitri M, Couturier S, Nguyen PH, Kamphuis R, Lacarriere B, Le Corre O
Energy and Buildings, 138, 240, 2017 |
| 8 |
Energy consumption prediction of air-conditioning systems in buildings by selecting similar days based on combined weights
Ma ZJ, Song JL, Zhang JL
Energy and Buildings, 151, 157, 2017 |
| 9 |
Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model
Yuan CQ, Liu SF, Fang ZG
Energy, 100, 384, 2016 |
| 10 |
Energy consumption predicting model of VRV (Variable refrigerant volume) system in office buildings based on data mining
Zhao DY, Zhong M, Zhang X, Su X
Energy, 102, 660, 2016 |
