IEICE TRANSACTIONS on Information
Illumination Modeling Method for Office Lighting Control by Using RBFNN
Summary :
This paper represents an illumination modeling method for lighting control which can model the illumination distribution inside office buildings. The algorithm uses data from the illumination sensors to train Radial Basis Function Neural Networks (RBFNN) which can be used to calculate 1) the illuminance contribution from each luminaire to different positions in the office 2) the natural illuminance distribution inside the office. This method can be used to provide detailed illumination contribution from both artificial and natural light sources for lighting control algorithms by using small amount of sensors. Simulations with DIALux are made to prove the feasibility and accuracy of the modeling method.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.12 pp.3192-3200
- Publication Date
- 2014/12/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2013EDP7384
- Type of Manuscript
- PAPER
- Category
- Biocybernetics, Neurocomputing
Authors
Wa SI
Waseda University
Xun PAN
Waseda University
Harutoshi OGAI
Waseda University
Katsumi HIRAI
Hakutsu Technology Ltd.
Noriyoshi YAMAUCHI
Waseda University
Tansheng LI
Waseda University
Keyword
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Wa SI, Xun PAN, Harutoshi OGAI, Katsumi HIRAI, Noriyoshi YAMAUCHI, Tansheng LI, "Illumination Modeling Method for Office Lighting Control by Using RBFNN" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 12, pp. 3192-3200, December 2014, doi: 10.1587/transinf.2013EDP7384.
Abstract: This paper represents an illumination modeling method for lighting control which can model the illumination distribution inside office buildings. The algorithm uses data from the illumination sensors to train Radial Basis Function Neural Networks (RBFNN) which can be used to calculate 1) the illuminance contribution from each luminaire to different positions in the office 2) the natural illuminance distribution inside the office. This method can be used to provide detailed illumination contribution from both artificial and natural light sources for lighting control algorithms by using small amount of sensors. Simulations with DIALux are made to prove the feasibility and accuracy of the modeling method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2013EDP7384/_p
Copy
@ARTICLE{e97-d_12_3192,
author={Wa SI, Xun PAN, Harutoshi OGAI, Katsumi HIRAI, Noriyoshi YAMAUCHI, Tansheng LI, },
journal={IEICE TRANSACTIONS on Information},
title={Illumination Modeling Method for Office Lighting Control by Using RBFNN},
year={2014},
volume={E97-D},
number={12},
pages={3192-3200},
abstract={This paper represents an illumination modeling method for lighting control which can model the illumination distribution inside office buildings. The algorithm uses data from the illumination sensors to train Radial Basis Function Neural Networks (RBFNN) which can be used to calculate 1) the illuminance contribution from each luminaire to different positions in the office 2) the natural illuminance distribution inside the office. This method can be used to provide detailed illumination contribution from both artificial and natural light sources for lighting control algorithms by using small amount of sensors. Simulations with DIALux are made to prove the feasibility and accuracy of the modeling method.},
keywords={},
doi={10.1587/transinf.2013EDP7384},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Illumination Modeling Method for Office Lighting Control by Using RBFNN
T2 - IEICE TRANSACTIONS on Information
SP - 3192
EP - 3200
AU - Wa SI
AU - Xun PAN
AU - Harutoshi OGAI
AU - Katsumi HIRAI
AU - Noriyoshi YAMAUCHI
AU - Tansheng LI
PY - 2014
DO - 10.1587/transinf.2013EDP7384
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2014
AB - This paper represents an illumination modeling method for lighting control which can model the illumination distribution inside office buildings. The algorithm uses data from the illumination sensors to train Radial Basis Function Neural Networks (RBFNN) which can be used to calculate 1) the illuminance contribution from each luminaire to different positions in the office 2) the natural illuminance distribution inside the office. This method can be used to provide detailed illumination contribution from both artificial and natural light sources for lighting control algorithms by using small amount of sensors. Simulations with DIALux are made to prove the feasibility and accuracy of the modeling method.
ER -
English
