Browse > Article

A Study of Optimum Control in Building HVAC System using Reinforce Signal  

Cho Sung-Hwan (Building Energy Research Center, KIER)
Yang Sung-Hee (Department of Architecture Engineering, Hanyang University)
Yang Hooncheul (Building Energy Research Center, KIER)
Publication Information
Korean Journal of Air-Conditioning and Refrigeration Engineering / v.16, no.11, 2004 , pp. 1068-1076 More about this Journal
Abstract
Technology on the proportional integral (PI) control have grown rapidly owing to the needs for the robust capacity of the controllers from industrial building sectors. However, PI controller requires tuning of gains for optimal control when the outside weather condition changes. The present study presents the possibility of reinforcement learning (RL) control algorithm with PI controller adapted in the HVAC system. The optimal design criteria of RL controller was proposed in the Environment Chamber experiment and a theoretical analysis was also conducted using TRNSYS program.
Keywords
Reinforcement learning; Optimal control; Auto-tuning; Building automation system; PI control;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Sutton, R. S, 1988, Learning to predict by the method of temporal difference, Machine Learning, Vol. 9, pp.9-44
2 So, J. H., Cho, S. H., Song, M. H. and Park,M. S., 2001, Experimental study on control performance of reinforcement learning method,Proceedings of the SAREK, pp.697-701
3 Hang, C. C. and Astrom, K.J. and Ho, W.K., 1991, Refinements of the Ziegler-Nichols tuning formula, IEE Proceedings Part DControl Theory Applicat., Vol. 138, No.2, pp.111-118
4 Anderson, C. W., Hittle, D. C., Katz, A. D. and Kretchmar, R. M., 1997, Synthesis of reinforcement learning, neural networks, and PI control applied to a simulated heating coil.Artificial Intelligence in Engineering, Vol. 11,No.4, pp. 421-429
5 Virk, G. S. and Loveday, D. L., 1992, A comparison of predictive, PID, and on/off techniques for energy management and control, Proceedings of ASHRAE, pp. 3-10
6 Sutton, R. S. and Barto, A. G., 1998, Reinforcement Learning: an Introduction, Cambridge, MA: MIT Press, pp. 51-85
7 Anderson, C. W., 1993, Q-learning with hidden-unit restarting, Advances in Neural Information Processing Systems, Vol. 5, S.].Hanson, J. D. Cowan and C. L. Giles, eds.,Morgan Kaufmann Publishers, San Mateo,CA, pp. 81-88
8 Watkins, C. and Dayan, P., 1992, Technical note: Q-learning, Machine Learning, Vol. 8,pp. 279-292
9 Ministry of Commerce, Industry and Energy,2003, Total energy consumption report, pp.1-80
10 Barto, A. G., Bradtke, S.]. and Singh, S. P.,1995, Learning to act using real-time dynamic programming, Artificial Intelligence,Special Volume: Computational Research on Interaction and Agency, Vol. 72, No.1, pp.81-138