A Study on Determining the Optimal Stop Time of a Heating System

  • Yang, In-Ho (Department of Architectural Engineerig, Dongguk University)
  • Published : 2005.03.01

Abstract

The purpose of this study is to present a method to determine the optimal stop time of HVAC using the Artificial Neural Network model, which is one of the learning methods. For this, the performance of determining the stop time of HVAC for unexperienced learning data was evaluated, and time interval for measurement of input data and permissible error needed for practical application of ANN model were presented using the results from daily simulation.

Keywords

References

  1. Nobuo Nakahara, 1981, Research and development on optimalization control of air-conditioning system-Part 1. Significance and methodology of optimization, Transaction of the Society of Heating Air-Conditioning and Sanitary Engineers of Japan, No. 17, p. 67
  2. TRANE, 1990, TRACE 600 Engineers manual, p.43
  3. Rumelhart, D. E. and Mcclelland, J. L., 1986, Parrel Distributed Processing, Vol. 1 & 2, MIT Press, Cambridge
  4. Curtiss, P. S., 1992, Artificial neural networks for use in building systems control and energy management, A dissertation for the degree of Ph. D, The University of Colorado
  5. Miller, R. C. and Seem, J. E., 1991, Comparison of artificial neural networks with traditional methods of predicting return time from night or weekend setback, ASHRAE Transaction, IN-91-1-4
  6. Lee, J.-Y., Yang, I.-H. and Kim, K.-W., 1991, A study on the predictive control of the Ondol system in apartments, Proceedings of Building Simulation '99, Vol. I, 6th International IBPSA Conference
  7. Yang, I.-H. and Kim, K.-W., 2004, Prediction of the time of room air temperature descending for heating systems in buildings, Building and Environment, Vol. 39, Issue 1
  8. NIST 1983, An optimum start/stop control algorithm for heating and cooling systems in building, p. 11