Simulation-based Evolutionary Multi-objective Optimization of Air conditioning Schedule in Office Building

• Yoshihiro Ohta

  Mitsubishi Electric Building Techno-Service Co., Ltd., The University of Electro-Communications

• Sato Hiroyuki

  The University of Electro-Communications

Bibliographic Information

Other Title

• オフィスビルにおける空調スケジュールのシミュレーションに基づく進化型多目的最適化

Abstract

For air-conditioning systems in office buildings, it is crucial to reduce power consumption while maintaining office workers' thermal comfort. This paper proposes a simulation-based evolutionary multi-objective air-conditioning schedule optimization system for office buildings. In the proposed system, a target office building is modeled and simulated by EnergyPlus building simulator which is one of the practical simulators widely used in the building construction field. To obtain the temperature schedules which dynamically change the temperature setting over time, we use an improved multi-objective particle swarm optimization algorithm, OMOPSO, to simultaneously optimize the thermal comfort of office workers in the building and the power consumption of the air-conditioning system. Experimental results show that the proposed system can obtain temperature schedules better than the conventional schedule with constant temperature settings from viewpoints of both the thermal comfort and the power consumption. Also, we show experimental results that the multi-objective search in the proposed system acquires better temperature schedules than single objective particle swarm optimization and differential evolution algorithms using ε-constraint method as one option of single objective optimization approaches. Furthermore, we show that OMOPSO obtains temperature schedules widely approximating the optimal tradeoff between the thermal comfort and the power consumption compared with other evolutionary multi-objective optimizers, NSGA-II, NSGA-III, MOEA/D-DE.

Journal

• Transaction of the Japanese Society for Evolutionary Computation

  Transaction of the Japanese Society for Evolutionary Computation 10 (2), 22-32, 2019

  The Japanese Society for Evolutionary Computation

Details

• CRID: 1390846609805270912
• NII Article ID: 130007797839
• DOI: 10.11394/tjpnsec.10.22
• ISSN: 21857385
• Text Lang: ja
• Data Source:
  • JaLC
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed