A novel energy saving system for office lighting control by using RBFNN and PSO

Wa Si, Harutoshi Ogai, Tansheng Li, Katsumi Hirai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

This paper represents a novel energy saving system for office lighting control which consists of LED lamps, one illumination sensor for measuring the natural illumination condition, and one control module for the integrated control. The control module embeds an intelligent algorithm for generating the optimized dimming pattern according to the natural illumination and occupancy condition. The intelligent algorithm contains 1) Radial Basis Function Neural Networks (RBFNN) which are used to calculate the illuminance contribution from each luminaire to different positions in the office 2) a PSO algorithm which is used to optimize dimming ratio for luminaires according to the target illuminance in occupied areas thus provide optimized control strategy for the office. Simulations are made to prove the feasibility and effectiveness of the illumination simulator.

Original languageEnglish
Title of host publicationIEEE 2013 Tencon - Spring, TENCONSpring 2013 - Conference Proceedings
Pages347-351
Number of pages5
DOIs
Publication statusPublished - 2013 Sep 16
Event2013 1st IEEE TENCON Spring Conference, TENCONSpring 2013 - Sydney, NSW, Australia
Duration: 2013 Apr 172013 Apr 19

Publication series

NameIEEE 2013 Tencon - Spring, TENCONSpring 2013 - Conference Proceedings

Conference

Conference2013 1st IEEE TENCON Spring Conference, TENCONSpring 2013
CountryAustralia
CitySydney, NSW
Period13/4/1713/4/19

Keywords

  • Energy Saving System
  • Office Lighting
  • Particle Swarm Optimization
  • Radial Basis Function Neural Networks

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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