A study on energy consumption of elevator group supervisory control systems using genetic network programming

  • Authors:

  • Lu Yu;Shingo Mabu;Tiantian Zhang;Kotaro Hirasawa;Tsuyoshi Ueno

  • Affiliations:

  • Graduate School of Information, Production and Systems, Waseda University, Kitakyushu, Fukuoka, Japan;Graduate School of Information, Production and Systems, Waseda University, Kitakyushu, Fukuoka, Japan;Graduate School of Information, Production and Systems, Waseda University, Kitakyushu, Fukuoka, Japan;Graduate School of Information, Production and Systems, Waseda University, Kitakyushu, Fukuoka, Japan;Central Research Institute of Electric Power Industry, Tokyo, Japan

  • Venue:
  • SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
  • Year:
  • 2009

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Abstract

Elevator group supervisory control system (EGSCS) is a traffic system, where its controller manages the elevator movement to transport passengers in buildings efficiently. Recently, Artificial Intelligence (AI) technology has been used in such complex systems. Genetic Network Programming(GNP), a graph-based evolutionary method extended from GA and GP, has been already applied to EGSCS. On the other hand, since energy consumption is becoming one of the greatest challenges in the society, it should be taken as criteria of the elevator operations. Moreover, the elevator with maximum energy efficiency is therefore required. Finally, the simulations show that the elevator system has the higher energy consumption in the light traffic, thus, some factors have been introduced into GNP for energy saving in this paper.