International Journal of Air-Conditioning and Refrigeration

The Society of Air-Conditioning and Refrigerating Engineers of Korea (대한설비공학회)
권호 표지

The Study on Cooling Load Forecast of an Unit Building using Neural Networks

  • Shin, Kwan-Woo (Department of Electrical Engineering, Kongju National University) ;
  • Lee, Youn-Seop (Department of Electrical Engineering, Kongju National University)
  • Published : 2003.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

The electric power load during the summer peak time is strongly affected by cooling load, which decreases the preparation ratio of electricity and brings about the failure in the supply of electricity in the electric power system. The ice storage system and heat pump system etc. are used to settle this problem. In this study, the method of estimating temperature and humidity to forecast the cooling load of ice storage system is suggested. The method of forecasting the cooling load using neural network is also suggested. The daily cooling load is mainly dependent on actual temperature and humidity of the day. The simulation is started with forecasting the temperature and humidity of the following day from the past data. The cooling load is then simulated by using the forecasted temperature and humidity data obtained from the simulation. It was observed that the forecasted data were closely approached to the actual data.

Keywords

References

  1. Mitchel, J. W., 1995, The control of ice-storage system, ASHRAE Transaction, CH-95-22-3, pp.1345-1352
  2. Gregor Pitter Daniel Dominikus Henze, 1997, Evaluation of Optimal Control for Ice Storage Systems, UMI dissertation
  3. Mortensen, R. E., 1988, A stochastic computer model for heating and cooling loads, IEEE Transaction on Power System, Vol. 3, No.3, pp. 1213-1217
  4. Hagan, M. T., 1987, The time series approach to short term load forecasting, IEEE Transaction on Power System, Vol.PWRS-2, No.3, pp.785-791
  5. Shin-Tzo Chan, 1992, Weather sesitivity short term load forecast using nonfully connected artificial neural network, IEEE Transaction on Power System, Vol. 7, No.3, pp. 1098-1105
  6. Lee, Y. S., 1990, Accelerating the convergency and improving the learning accuracy of BP neural network, KIEE, Vol. 39, No.8, pp. 856-867
  7. So, S. S. and Jung, C. H., 1990, Meteorological dynamics an introduction, Kyohakyunkusa, pp. 57-88