| 1 |
Design of machine learning models with domain experts for automated sensor selection for energy fault detection
Hu RL, Granderson J, Auslander DM, Agogino A
Applied Energy, 235, 117, 2019 |
| 2 |
Statistical change detection of building energy consumption: Applications to savings estimation
Touzani S, Ravache B, Crowe E, Granderson J
Energy and Buildings, 185, 123, 2019 |
| 3 |
Integrating diagnostics and model-based optimization
Granderson J, Lin GJ, Blum D, Page J, Spears M, Piette MA
Energy and Buildings, 182, 187, 2019 |
| 4 |
Evaluation of methods to assess the uncertainty in estimated energy savings
Touzani S, Granderson J, Jump D, Rebello D
Energy and Buildings, 193, 216, 2019 |
| 5 |
A performance evaluation framework for building fault detection and diagnosis algorithms
Frank S, Lin GJ, Jjn X, Singla R, Farthing A, Granderson J
Energy and Buildings, 192, 84, 2019 |
| 6 |
Gradient boosting machine for modeling the energy consumption of commercial buildings
Touzani S, Granderson J, Fernandes S
Energy and Buildings, 158, 1533, 2018 |
| 7 |
A framework for monitoring-based commissioning: Identifying variables that act as barriers and enablers to the process
Harris N, Shealy T, Kramer H, Granderson J, Reichard G
Energy and Buildings, 168, 331, 2018 |
| 8 |
Field evaluation of performance of HVAC optimization system in commercial buildings
Granderson J, Lin GJ, Singla R, Fernandes S, Touzani S
Energy and Buildings, 173, 577, 2018 |
| 9 |
Application of automated measurement and verification to utility energy efficiency program data
Granderson J, Touzani S, Fernandes S, Taylor C
Energy and Buildings, 142, 191, 2017 |
| 10 |
Accuracy of automated measurement and verification (M&V) techniques for energy savings in commercial buildings
Granderson J, Touzani S, Custodio C, Sohn MD, Jump D, Fernandes S
Applied Energy, 173, 296, 2016 |
