Illumination modeling method for office lighting control by using RBFNN

Wa Si; Xun Pan; Harutoshi Ogai; Katsumi Hirai; Noriyoshi Yamauchi; Tansheng Li

doi:10.1587/transinf.2013EDP7384

Illumination modeling method for office lighting control by using RBFNN

Wa Si, Xun Pan, Harutoshi Ogai, Katsumi Hirai, Noriyoshi Yamauchi, Tansheng Li

Graduate School of Information, Production and Systems

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

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.

Original language	English
Pages (from-to)	3192-3200
Number of pages	9
Journal	IEICE Transactions on Information and Systems
Volume	E97D
Issue number	12
DOIs	https://doi.org/10.1587/transinf.2013EDP7384
Publication status	Published - 2014 Dec 1

Keywords

Energy saving system
Office lighting
Radial basis function neural networks

ASJC Scopus subject areas

Software
Hardware and Architecture
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering
Artificial Intelligence

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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.",

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author = "Wa Si and Xun Pan and Harutoshi Ogai and Katsumi Hirai and Noriyoshi Yamauchi and Tansheng Li",

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AU - Si, Wa

AU - Pan, Xun

AU - Ogai, Harutoshi

AU - Hirai, Katsumi

AU - Yamauchi, Noriyoshi

AU - Li, Tansheng

PY - 2014/12/1

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N2 - 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.

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.

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KW - Office lighting

KW - Radial basis function neural networks

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Illumination modeling method for office lighting control by using RBFNN

Abstract

Keywords

ASJC Scopus subject areas

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