A method to predict cooling load of large commercial buildings based on weather forecast and internal occupancy

Abstract

Considering the fact that customers of large commercial buildings have the characteristics of the higher density and randomness, this paper presented an air-conditioning cooling load prediction method based on weather forecast and internal occupancy density. The multiple linear feedback regression model was applied to predict, with precision, the air conditioning cooling load. Case analysis showed that the largest mean relative error of hourly and the daily predicting cooling load maximum were 18.1% and 5.14%, respectively.

Keywords

commercial building / load prediction / multiple linear regression

References

[1] Zhao J, Wu Y. Theory and practice on building energy efficiency in China. Energy Policy, 2009, 37(6): 2053 (in Chinese)

[2] Census and Statistics Department, Hong Kong Government. Hong Kong energy statistics 1989–2000. <Date>2015–06–03</Date> in Chinese)

[3] Liu C, Wang R. Correlation analysis on influencing factors of cooling load. Refrigeration and Air Conditioning, 2015, 29(2): 179–182 (in Chinese)

[4] Zhao B, Wen Y, Huan Y, Hou Z. Analysis and compare about four kinds of air conditioning load prediction method. Journal of Architectural Ventilation and Air Conditioning Heat, 2011, 30(6): 65–67 (in Chinese)

[5] Yong W, Huang G. Peng D. Multiple linear regression method with feedback in the application of power load forecasting. Journal of Computer Applications and Software, 2008, 5(1): 82–84 (in Chinese)

[6] Zhao B, Wen Y, Hou Z. An improved air conditioning load prediction mode by multiple linear regression. Journal of Refrigeration and Air Conditioning and Electric Machinery, 2011, 32(4): 49–51 (in Chinese)

[7] Liu J. The research about the ice storage cold air conditioning load prediction based on wavelet analysis and neural network. Dissertation for the Master’s Degree. Harbin Industrial University Municipal Environmental Engineering Institute, 2007 (in Chinese)

[8] Kawashima M, Dorgan C E, Mitchell J W. Hourly thermal load prediction for the next 24 hours by ARIMA, EWA, LR and an artificial neural network. ASHRAE Transactions, 1995, 101(1): 186–200

[9] Lei F, Tan C, Lu S. Statistical analysis and modeling about personnel density in commercial building. Journal of Fire Sciences, 2009, 19(3): 130–137 (in Chinese)

[10] Guo W, Liu T, Zhang Y. The data analysis about occupancy density survey results of large commercial buildings. In: Proceedings of the Science and Technology Annual Meeting of China Fire Protection Association. Guangzhou, China, 2012 (in Chinese)

[11] Sun J, Cheng D. Air conditioning load forecasting based on seasonal time series model. Journal of Intelligent Building and City Information, 2004, 12(3): 88–93 (in Chinese)

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag Berlin Heidelberg