Building Indoor Temperature Control Using PSO Algorithm

PSO 알고리즘을 이용한 건물 실내온도 제어

  • Kim, Jeong-Hyuk (Department of Electrical Engineering, Jeju National University) ;
  • Kim, Ho-Chan (Department of Electrical Engineering, Jeju National University)
  • Received : 2013.04.02
  • Accepted : 2013.05.09
  • Published : 2013.05.31


In this paper, we proposed the modeling in one zone buildings and the energy efficient temperature control algorithm using particle swarm optimization (PSO). A control horizon switching method with PSO is used for optimal control, and the TOU tariff is included to calculate the energy costs. Simulation results show that the reductions of energy cost and peak power can be obtained using proposed algorithms.


Energy saving;Builind modeling;Particle swarm optimization;control horizon;building indoor temperature


Supported by : 제주대학교


  1. Ministry of Land, Transport and Maritime Affairs, 녹색건축물 조성 지원법, 2013.
  2. N. Mendes, G. H. C. Oliveira, H. X. Araujo, and L. S. Coelho, "A Matlabbased simulation tool for building thermal performance analysis," In Eighth International IBPSA Conference, Eindhoven, Netherlands, August 11-14, 2003.
  3. I. Beausoleil Morrison, F. Macdonald, M. Kummert, T. McDowell, R. Jost, and A. Ferguson, "The design of an ESPr and TRNSYS cosimulator," In Proc. Building Simulation 2011, pp. 2333-2340, 2011.
  4. A. W. M. van. Schijndel, Integrated Heat Air and Moisture Modeling and Simulation, PhD Dissertation, Eindhoven University of Technology, 2007.
  5. C.-J. Boo, J.-H. Kim, and H.-C. Kim, "Building indoor temperature control using control horizon method in cooling systems", Journal of the Korean Academia-Industrial cooperation Society, vol. 13, no. 10, pp. 4902-4909, 2012. DOI:
  6. J.S. Heo, K.Y. Lee and R. Garduno-Ramirez: Multiobjective control of power plants using particle swarm optimization techniques. IEEE Transactions on Energy Conversion, vol. 21, pp. 552-561, 2006. DOI:
  7. I. Hazyuk, C. Ghiaus, and D. Penhouet, "Optimal temperature control of intermittently heated buildings using model predictive control: Part I - Building modeling", Building and Environment, vol. 51, pp. 379-387, 2012. DOI:
  8. Y. Yang, A. Pinto, A. Sangiovanni-Vincentelli, and Q. Zhu, "A design flow for building automation and control systems", 31st IEEE Real-Time Systems Symposium (RTSS'10), pp. 105-116, San Diego, CA, December 2010. DOI:
  9. C.-B. Park, A Study on the Application of Low Energy Cooling System in Office Building, PhD Dissertation, Chung-Ang University, 2011.
  10. Korea Institute of Energy Technology Evaluation and Planning, Green Energy Strategy Load Map, 2011.