Local urban weather data generation based on atmospheric reanalysis data to support building energy design in Singapore

Conference Paper (2024)
Author(s)

Guanli Feng (City University of Hong Kong)

Xing Zheng (City University of Hong Kong, Future Cities Laboratory Global)

Naika Meili (Future Cities Laboratory Global)

Shuyang Li (National University of Singapore, Future Cities Laboratory Global)

Martín Mosteiro-Romero (TU Delft - Environmental & Climate Design)

Zhen Han (Tianjin University)

Lei Xu (Future Cities Laboratory Global, University of Cambridge)

Dengkai Chi (Future Cities Laboratory Global)

Rudi Stouffs (National University of Singapore, Future Cities Laboratory Global)

Research Group
Environmental & Climate Design
More Info
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Publication Year
2024
Language
English
Research Group
Environmental & Climate Design
Pages (from-to)
404-410
Downloads counter
217
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Abstract

Building energy modeling (BEM) is essential for predicting energy use and improving thermal performance in buildings. Traditionally, weather data for BEM comes from built-in tool datasets. Additionally, global atmospheric reanalysis datasets like ERA5, have been used in recent years for BEM. However, the spatial resolution of global atmospheric reanalysis datasets is generally coarse relative to cities, limiting their accuracy in capturing local urban climate effects. Adopting ERA5 as the forcing data, this study examines the use of two urban land surface models, Urban Tethys-Chloris (UT&C) and Urban Weather Generator (UWG), to generate localized weather data for Singapore. The generated local weather data are compared with the data from an on-campus weather station and other weather datasets. Subsequently, these weather datasets are employed as input for an educational building’s energy model that has been validated with energy meter data. The results demonstrate a better agreement between the generated local weather data and locally measured data, compared to the original ERA5 data and typical meteorological year weather data. This leads to an improved accuracy in building energy prediction. By leveraging the global availability of atmospheric reanalysis datasets, this framework for generating local weather data can serve as a universally applicable approach to support building energy design in tropical cities.

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