| 1 |
A novel method for fast sky conditions identification from global solar radiation measurements
Lou SW, Huang Y, Li DHW, Xia DW, Zhou XQ, Zhao Y
Renewable Energy, 161, 77, 2020 |
| 2 |
Identifying overcast, partly cloudy and clear skies by illuminance fluctuations
Lou SW, Li DHW, Chen WQ
Renewable Energy, 138, 198, 2019 |
| 3 |
Evaluation of classification methods according to solar radiation features from the viewpoint of the production of parabolic trough CSP plants
Moreno-Tejera S, Silva-Perez MA, Ramirez-Santigosa L, Lillo-Bravo I
Renewable Energy, 121, 429, 2018 |
| 4 |
Accuracy analysis of computational algorithms for prediction of daylight illuminance in space with shading devices
Yoon Y, Moon JW, Alzoubi HH, Kim S
Solar Energy, 153, 700, 2017 |
| 5 |
Development of annual daylight simulation algorithms for prediction of indoor daylight illuminance
Yoon Y, Moon JW, Kim S
Energy and Buildings, 118, 1, 2016 |
| 6 |
Artificial illumination during daytime in residential buildings: Factors, energy implications and future predictions
Das A, Paul SK
Applied Energy, 158, 65, 2015 |
| 7 |
Analysis of ultraviolet radiation in Central China from observation and estimation
Wang LC, Gong W, Ma YY, Hu B, Wang WL, Zhang M
Energy, 59, 764, 2013 |
| 8 |
A simple evaluation of global and diffuse luminous efficacy for all sky conditions in tropical and humid climate
Fakra AH, Boyer H, Miranville F, Bigot D
Renewable Energy, 36(1), 298, 2011 |
| 9 |
INLUX-DBR - A calculation code to calculate indoor natural illuminance inside buildings under various sky conditions
Ferraro V, Igawa N, Marinelli V
Energy, 35(9), 3722, 2010 |
| 10 |
An assessment of a revised Olmo et al. model to predict solar global radiation on a tilted surface at Beer Sheva, Israel
Evseev EG, Kudish AI
Renewable Energy, 34(1), 112, 2009 |
