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Development of a Numerical Weather Analysis Tool for Assessing the Precooling Potential at Any Location

Author

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  • Dimitris Lazos

    (School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Sydney, NSW 2052, Australia)

  • Merlinde Kay

    (School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Sydney, NSW 2052, Australia)

  • Alistair Sproul

    (School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Sydney, NSW 2052, Australia)

Abstract

Precooling a building overnight during the summer is a low cost practice that may provide significant help in decreasing energy demand and shaving peak loads in buildings. The effectiveness of precooling depends on the weather patterns at the location, however research in this field is predominantly focused in the building thermal response alone. This paper proposes an analytical tool for assessing the precooling potential through simulations from real data in a numerical weather prediction platform. Three dimensionless ratios are developed based on the meteorological analysis and the concept of degree hours that provide an understanding of the precooling potential, utilization and theoretical value. Simulations were carried out for five sites within the Sydney (Australia) metro area and it was found that they have different responses to precooling, depending on their proximity to the ocean, vegetation coverage, and urban density. These effects cannot be detected when typical meteorological year data or data from weather stations at a distance from the building were used. Results from simulations in other Australian capitals suggest that buildings in continental and temperate climates have the potential to cover substantial parts of the cooling loads with precooling, assuming appropriate infrastructure is in place.

Suggested Citation

  • Dimitris Lazos & Merlinde Kay & Alistair Sproul, 2016. "Development of a Numerical Weather Analysis Tool for Assessing the Precooling Potential at Any Location," Energies, MDPI, vol. 10(1), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:10:y:2016:i:1:p:21-:d:86113
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    References listed on IDEAS

    as
    1. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    2. Lazos, Dimitris & Sproul, Alistair B. & Kay, Merlinde, 2014. "Optimisation of energy management in commercial buildings with weather forecasting inputs: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 587-603.
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