| 1 |
Carbon dioxide-based occupancy estimation using stochastic differential equations
Wolf S, Cali D, Krogstie J, Madsen H
Applied Energy, 236, 32, 2019 |
| 2 |
Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks
Wei YX, Xia L, Pan S, Wu JS, Zhang XX, Han MJ, Zhang WY, Xie JC, Li QP
Applied Energy, 240, 276, 2019 |
| 3 |
A decision tool to balance indoor air quality and energy consumption: A case study
Pantazaras A, Santamouris M, Lee SE, Assimakopoulos MN
Energy and Buildings, 165, 246, 2018 |
| 4 |
A novel feature selection framework with Hybrid Feature-Scaled Extreme Learning Machine (HFS-ELM) for indoor occupancy estimation
Masood MK, Jiang CY, Soh YC
Energy and Buildings, 158, 1139, 2018 |
| 5 |
Building occupancy estimation and detection: A review
Chen ZH, Jiang CY, Xie LH
Energy and Buildings, 169, 260, 2018 |
| 6 |
Comparative assessment of low-complexity models to predict electricity consumption in an institutional building: Linear regression vs. fuzzy modeling vs. neural networks
Pombeiro H, Santos R, Carreira P, Silva C, Sousa JMC
Energy and Buildings, 146, 141, 2017 |
| 7 |
Indoor occupancy estimation from carbon dioxide concentration
Jiang CY, Masood MK, Soh YC, Li H
Energy and Buildings, 131, 132, 2016 |
| 8 |
A fusion framework for occupancy estimation in office buildings based on environmental sensor data
Chen ZH, Masood MK, Soh YC
Energy and Buildings, 133, 790, 2016 |
| 9 |
Incorporation of scheduling and adaptive historical data in the Sensor-Utility-Network method for occupancy estimation
Ryan T, Vipperman JS
Energy and Buildings, 61, 88, 2013 |
