Browse > Article

Study on the Feasibility of Applying Forecasted Weather Data for Operations of a Thermal Storage System  

Jung Jae-Hoon (Sejong-Lockheed Martin Aerospace Research Center, Sejong University)
Shin Young-Gy (Department of Mechanical Engineering, Sejong University)
Park Byung-Yoon (Department of Architecture, Suwon Science College)
Publication Information
Korean Journal of Air-Conditioning and Refrigeration Engineering / v.18, no.1, 2006 , pp. 87-94 More about this Journal
Abstract
In this paper, we investigated a feasibility of applying highest and lowest temperatures of the next day forecasted from a meteorological observatory to operation of an air-conditioning system with thermal storage. First we investigated specific characteristics of the time series of forecasted temperatures and errors in Osaka from 1994 to 1996. Since the forecast error is not always small, it might be difficult to use the forecasted data without correction for the sizing and the control of the thermal storage system. On the other hand, the autocorrelation functions of the forecast errors decrease relatively slowly during high summer season when cooling thermal storage is required. Since the values of the autocorrelation function; for one day are larger than 0.4, not small, the forecast errors can be predicted by proper statistical analysis. Thus, the forecasted values of the highest temperatures for the next day were improved by using the stochastic time series models.
Keywords
Forecasted weather data; Forecast error; Autocorrelation function; Stochastic time series model;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Yoshida, H. N. and Terai, T. O., 1992, Modeling of weather data by time series analysis for air-conditioning load calculations, ASHRAE Transactions, Vol. 98, No. 1, pp. 328-345
2 www.kma.go.kr
3 Jung, J. H., 1997, Study on the Optimal Control of a Thermal Storage System Using the Weather Forecast, MS thesis, University of Kyoto, Kyoto, Japan
4 Han, D. Y. and Youn, H. B., 2002, Building energy control algorithms by using outdoor air temperature prediction, Proceedings of the SAREK 2002 Winter Annual Conference, pp. 345-350
5 Yoshida, H. N., 1997, Heating and cooling load prediction for the rational management of thermal : storage tank operation, Journal of Architecture, Planning and Environmental Engineering (Transactions of AIJ), No. 495, pp. 77-83
6 Box, G. E. P. and Jenkins, G. M., 1976, Time Series Analysis - Forecasting and Control, Holden-Day
7 Hokoi, S. I., Matsumoto, M. and Ihara, T. K., 1991, Statistical time series models of solar radiation and outdoor temperature - Identification of seasonal models by Kalman filter, Energy and Buildings, Vol. 15, No. 16, pp. 373-383   DOI   ScienceOn
8 Hokoi, S. I. and Matsumoto, M., 1988, An analysis of stochastic properties of the heating load in an intermittently air-conditioned building, Energy and Buildings, Vol. 11, pp. 259-266   DOI   ScienceOn