| 1 |
Assessment of indoor illuminance and study on best photosensors' position for design and commissioning of Daylight Linked Control systems. A new method based on artificial neural networks
Beccali M, Bonomolo M, Ciulla G, Lo Brano V
Energy, 154, 466, 2018 |
| 2 |
A preliminary study of occupants' use of manual lighting controls in private offices: A case study
Gilani S, O'Brien W
Energy and Buildings, 159, 572, 2018 |
| 3 |
WinLight: A WiFi-based occupancy-driven lighting control system for smart building
Zou H, Zhou YX, Jiang H, Chien SC, Xie LH, Spanos CJ
Energy and Buildings, 158, 924, 2018 |
| 4 |
Lighting energy efficiency in offices under different control strategies
Xu L, Pan YQ, Yao Y, Cai DD, Huang ZZ, Linder N
Energy and Buildings, 138, 127, 2017 |
| 5 |
One size does not fit all: Understanding user preferences for building automation systems
Ahmadi-Karvigh S, Ghahramani A, Becerik-Gerber B, Soibelman L
Energy and Buildings, 145, 163, 2017 |
| 6 |
A first approach to universal daylight and occupancy control system for any lamps: Simulated case in an academic classroom
de Rubeis T, Muttillo M, Pantoli L, Nardi I, Leone I, Stornelli V, Ambrosini D
Energy and Buildings, 152, 24, 2017 |
| 7 |
Smart lighting: The way forward? Reviewing the past to shape the future
Chew I, Karunatilaka D, Tan CP, Kalavally V
Energy and Buildings, 149, 180, 2017 |
| 8 |
Field data and simulations to estimate the role of standby energy use of lighting control systems in individual offices
Gentile N, Dubois MC
Energy and Buildings, 155, 390, 2017 |
| 9 |
Light-based circadian rhythm control: Entrainment and optimization
Zhang JX, Qiao W, Wen JT, Julius A
Automatica, 68, 44, 2016 |
| 10 |
Application of multi-objective genetic algorithms to interior lighting optimization
Madias END, Kontaxis PA, Topalis FV
Energy and Buildings, 125, 66, 2016 |
