Accessibility navigation


Urban meteorological forcing data for building energy simulation

Tang, Y., Sun, T. ORCID: https://orcid.org/0000-0002-2486-6146, Luo, Z. ORCID: https://orcid.org/0000-0002-2082-3958, Omidvar, H., Theeuwes, N., Xie, X., Xiong, J., Yao, R. and Grimmond, S. ORCID: https://orcid.org/0000-0002-3166-9415 (2021) Urban meteorological forcing data for building energy simulation. Building and Environment. 108088. ISSN 0360-1323

[img] Text - Accepted Version
· Restricted to Repository staff only until 1 July 2022.
· Available under License Creative Commons Attribution Non-commercial No Derivatives.

2MB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1016/j.buildenv.2021.108088

Abstract/Summary

Despite building energy use being one of the largest global energy consumers, building energy simulations rarely take the actual local neighbourhood scale climate into account. A new globally applicable approach is proposed to support buildings energy design. ERA5 (European Centre Reanalysis version 5) data are used with SUEWS (Surface Urban Energy and Water balance Scheme) to obtain (in this example case) an urban typical meteorological year (uTMY) that is useable in building energy modelling. The predicted annual energy demand (heating and cooling) for a representative four-storey London residential apartment using uTMY is 24.1% less (cf. conventional TMY). New vertical profile coefficients for wind speed and air temperature in EnergyPlus are derived using SUEWS. EneryPlus simulations with these neighbourhood scale coefficients and uTMY data, predict the top two floors have ∼40% larger energy demand (cf. the open terrain coefficients with uTMY data). Vertical variations in wind speed have a greater impact on the simulated building energy than equivalent variations in temperature. This globally appliable approach can provide local meteorological data for building energy modelling, improving design for the local context through characterising the surrounding neighbourhood.

Item Type:Article
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:99057
Publisher:Elsevier

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation