| 1 |
Bottom-up modelling methodology for urban-scale analysis of residential space heating demand response
Hedegaard RE, Kristensen MH, Pedersen TH, Brun A, Petersen S
Applied Energy, 242, 181, 2019 |
| 2 |
Chemo-thermal model and Gaussian process emulator for combustion synthesis of Ni/Al composites
Shabouei M, Subber W, Williams CW, Matous K, Powers JM
Combustion and Flame, 207, 153, 2019 |
| 3 |
Sequential Monte Carlo for on-line parameter estimation of a lumped building energy model
Rouchier S, Jimenez MJ, Castano S
Energy and Buildings, 187, 86, 2019 |
| 4 |
Continuous-time Bayesian calibration of energy models using BIM and energy data
Chong A, Xu WL, Chao S, Ngo NT
Energy and Buildings, 194, 177, 2019 |
| 5 |
Reduced-order model for microstructure evolution prediction in the electrodes of solid oxide fuel cell with dynamic discrepancy reduced modeling
Lei YK, Chen TL, Mebane DS, Wen YH
Journal of Power Sources, 416, 37, 2019 |
| 6 |
Influences of energy data on Bayesian calibration of building energy model
Lim H, Zhai ZQ
Applied Energy, 231, 686, 2018 |
| 7 |
An efficient Bayesian experimental calibration of dynamic thermal models
Raillon L, Ghiaus C
Energy, 152, 818, 2018 |
| 8 |
Hierarchical calibration of archetypes for urban building energy modeling
Kristensen MH, Hedegaard RE, Petersen S
Energy and Buildings, 175, 219, 2018 |
| 9 |
Guidelines for the Bayesian calibration of building energy models
Chong A, Menberg K
Energy and Buildings, 174, 527, 2018 |
| 10 |
Validation of a Bayesian-based method for defining residential archetypes in urban building energy models
Julia S, Carlos CD, Christoph FR
Energy and Buildings, 134, 11, 2017 |
