NDLI: Artificial neural network application to a building hybrid illumination system

Content Provider	IEEE Xplore Digital Library
Author	Cardoso, R.R. Brandao, A.F.
Copyright Year	2008
Description	Author affiliation: Dept of Electr. & Autom. Eng., Polytech. Sch. of the Univ. of Sao Paulo, Sao Paulo (Cardoso, R.R.; Brandao, A.F.)
Abstract	The search for new control systems which present advances in energy consumption programs has increased in the last decades, not only due to the widespread use of microprocessors in the different sectors of engineering but also to the advent of the techniques of artificial intelligence. The purpose of this paper is to show the possibility of the use and to analyze automated systems that make use of the artificial lighting in addition to the natural one. An algorithm based on neural computation was developed to analyze the energetic performance of this system, in a multi floor building employed for a commercial office. The hybrid lighting automated systems shall be analyzed taking in mind the influence of natural factors, constructive details of the building and others that define the energetic efficiency of the building. The use of neural computation makes possible a predictive model to operate these systems, increasing the availability and the reliability of the diagnostics, and is a computational tool with a large performance to the solution of problems that are very complex with the traditional mathematic modeling.
Starting Page	866
Ending Page	871
File Size	654167
Page Count	6
File Format	PDF
ISBN	9781424416653
DOI	10.1109/ISIE.2008.4677011
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-30
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Lighting Buildings Predictive models Control systems Energy consumption Microprocessors Power engineering and energy Artificial intelligence Performance analysis artificial neural networks lighting control hybrid lighting system electric energy efficiency
Content Type	Text
Resource Type	Article

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