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Fitting Analysis for Flow Coefficient Predictive Model of Residential Buildings with High Airtightness

  • Wenqian ZhouEmail author
  • Xiangli Li
  • Lin Duanmu
Conference paper
  • 241 Downloads
Part of the Environmental Science and Engineering book series (ESE)

Abstract

The flow coefficient is an important parameter of air infiltration, affected by many factors, such as building parameters, wind speed and temperature difference between indoor and outdoor. And its modeling is used to adopt the method, multiple linear regression, ignoring the problem caused by characteristic of small sample size. Stepwise regression, least squares and partial least squares (PLSs) with corresponding independent variable screening methods are adopted to obtain the predictive models which are compared with F-test, goodness of fit and CV (RMSE). The result shows that the goodness of fit of apartment’s model and villa’s model received by PLS is increased by 25.9% and 2.2%, respectively, compared with least squares and the PLS models’ CV (RMSE) which are 9.4% and 18.3%. Considering the essential characteristics and fitting results of the three methods, it can be concluded that PLS which is suitable for small sample size is the preferred choice for the establishment of flow coefficient model.

Keywords

Air infiltration Flow coefficient Data fitting 

Notes

Acknowledgements

This chapter is supported by the National Key Research and Development Program of China “Near-Zero Energy Building Technology System and Key Technology Development” (number 2017YFC0702600).

References

  1. 1.
    Ashrae.: ANSI/ASHRAE guideline 14-2014 measurement of energy and demand savings, Atlanta (2014)Google Scholar
  2. 2.
    Chiu, Y.H., Etheridge, D.W.: Calculations and notes on the quadratic and power law equations for modelling infiltration. Int. J. Vent. 1(1), 65–77 (2002)CrossRefGoogle Scholar
  3. 3.
    Colliver D.G., et al.: ASHRAE research project report RP-438: evaluation of the techniques for the measurement of air leakage of building components (1993) Google Scholar
  4. 4.
    Dick, J.B.: Experimental studies in natural ventilation of houses. J. Inst. Heating Ventilating Eng. (1949)Google Scholar
  5. 5.
    Etheridge, D.: A perspective on fifty years of natural ventilation research. Build. Environ. 91, 51–60 (2015)CrossRefGoogle Scholar
  6. 6.
    Ji, Y.: Study on Air Infiltration Prediction Model for Residential Buildings with High Airtightness Envelope in Cold Area. Dalian University of Technology (2018)Google Scholar
  7. 7.
    Sherman, M.H.: Building Airtightness: Research and Practice. Lawrence Berkeley National Laboratory, Berkeley (2004)Google Scholar
  8. 8.
    Wang, S., et al.: Error Theory and Surveying Adjustment. Tongji University Press (2010)Google Scholar
  9. 9.
    Wang, Y., et al.: Dictionary of Mathematics. Science Press (2010)Google Scholar
  10. 10.
    Xian, Y., et al.: Application of multiple-stepwise and Kalman Filtering in haze forecast. J. Syst. Simul. 30(04), 278–285 (2018)Google Scholar
  11. 11.
    Zhang, H., et al.: Analysis and Method of Small Sample Multivariate Data. Northwestern Polytechnical University Press (2002)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Dalian University of TechnologyDalianChina

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