| 1 |
A short-term building cooling load prediction method using deep learning algorithms
Fan C, Xiao F, Zhao Y
Applied Energy, 195, 222, 2017 |
| 2 |
Case study: Energy savings from solar window film in two commercial buildings in Shanghai
Yin RX, Xu P, Shen PY
Energy and Buildings, 45, 132, 2012 |
| 3 |
The use of occupancy space electrical power demand in building cooling load prediction
Leung MC, Tse NCF, Lai LL, Chow TT
Energy and Buildings, 55, 151, 2012 |
| 4 |
Study on simulation methods of atrium building cooling load in hot and humid regions
Pan YQ, Li YM, Huang ZZ, Wu G
Energy and Buildings, 42(10), 1654, 2010 |
| 5 |
A numerical simulation tool for predicting the impact of outdoor thermal environment on building energy performance
He J, Hoyano A, Asawa T
Applied Energy, 86(9), 1596, 2009 |
| 6 |
Applying support vector machine to predict hourly cooling load in the building
Li Q, Meng QL, Cai JJ, Yoshino H, Mochida A
Applied Energy, 86(10), 2249, 2009 |
| 7 |
Economic feasibility of thermal energy storage systems
Habeebullah BA
Energy and Buildings, 39(3), 355, 2007 |
| 8 |
Low-energy design for air-cooled chiller plants in air-conditioned buildings
Yu FW, Chan KT
Energy and Buildings, 38(4), 334, 2006 |
| 9 |
Energy signatures for assessing the energy performance of chillers
Yu FW, Chan KT
Energy and Buildings, 37(7), 739, 2005 |
| 10 |
An OTTV-based energy estimation model for commercial buildings in Thailand
Chirarattananon S, Taveekun J
Energy and Buildings, 36(7), 680, 2004 |
