| 1 |
Bayesian classification and inference of occupant visual preferences in daylit perimeter private offices
Sadeghi SA, Lee S, Karava P, Bilionis I, Tzempelikos A
Energy and Buildings, 166, 505, 2018 |
| 2 |
Economic, environmental and health co-benefits of the use of advanced control strategies for lighting in buildings of Mexico
Diaz-Mendez SE, Torres-Rodriguez AA, Abatal M, Soberanis MAE, Bassam A, Pedraza-Basulto GK
Energy Policy, 113, 401, 2018 |
| 3 |
Costs and impacts of potential energy strategies for rural households in developing communities
MacCarty NA, Bryden KM
Energy, 138, 1157, 2017 |
| 4 |
Minimizing computational cost and energy demand of building lighting systems: A real time experiment using a modified competition over resources algorithm
Mendes LA, Freire RZ, Coelho LD, Moraes AS
Energy and Buildings, 139, 108, 2017 |
| 5 |
Energy and reliability optimization of a system that combines daylighting and artificial sources. A case study carried out in academic buildings
Salata F, Golasi I, di Salvatore M, Vollaro AD
Applied Energy, 169, 250, 2016 |
| 6 |
ANN-based estimation of time-dependent energy loss in lighting systems
Sahin M, Oguz Y, Buyuktumturk F
Energy and Buildings, 116, 455, 2016 |
| 7 |
A survey of power supply and lighting patterns in North Central Nigeria-The energy saving potentials through efficient lighting systems
Ahemen I, Amah AN, Agada PO
Energy and Buildings, 133, 770, 2016 |
| 8 |
Spatiotemporal lighting load disaggregation using light intensity signal
Jazizadeh F, Ahmadi-Karvigh S, Becerik-Gerber B, Soibelman L
Energy and Buildings, 69, 572, 2014 |
| 9 |
Daylight-adaptive lighting control using light sensor calibration prior-information
Caicedo D, Pandharipande A, Willems FMJ
Energy and Buildings, 73, 105, 2014 |
| 10 |
Adoption of solar home lighting systems in India: What might we learn from Karnataka?
Harish SM, Iychettira KK, Raghavan SV, Kandlikar M
Energy Policy, 62, 697, 2013 |
