Abstract
These future building energy studies mainly stem from hourly based dynamic building simulation results with the future weather data. The reliability of the future building energy forecast heavily relies on the accuracy of these future weather data. The global circulation models (GCMs) provided by IPCC are the major sources for constructing future weather data. However, there are uncertainties existed among them even with the same climate change scenarios. There is a need to develop a method on how to select the suitable GCM for local application. This research firstly adopted principal component analysis (PCA) method in choosing the suitable GCM for application in Taiwan, and secondly the Taiwanese hourly future meteorological data sets were constructed based on the selected GCM by morphing method. Thirdly, the future cooling energy consumption of an actual office building in the near (2011-2040), the mid (2041-2070), and the far future (2071-2100), were analysed. The results show that NorESM1-M GCM has the lowest root mean square error (RMSE) as opposed to the other GCMs, and was identified as the suitable GCM for further future climate generation processing. The building simulation against the future weather datasets revealed that the average cooling energy use intensity (EUIc) in Taipei will be increased by 12%, 17%, and 34% in the 2020s, 2050s, and 2080s, respectively, as compared to the current climate.
Highlights
The constant changing climate due to global warming in recent years brings great attentions on the studies on the future building energy consumption trends, and desire to seek potential adaptation measures in order to minimize the excessive building energy usage impacted by the climate change
As there are several global circulation models (GCMs) provided by the Intergovernmental Panel on Climate Change (IPCC) and readily available, each provides monthly averaged projections of meteorological variables till the end of the 21st century with four climate change scenarios in the fifth assessment report (AR5), it is necessary to identify which GCM is suitable for application in Taiwan
We identified the Norwegian NorESM1-M is the best suitable GCM for application in Taiwan by the principal component analysis (PCA) approach
Summary
The constant changing climate due to global warming in recent years brings great attentions on the studies on the future building energy consumption trends, and desire to seek potential adaptation measures in order to minimize the excessive building energy usage impacted by the climate change. In the study of projecting future building energy use, the hourly future meteorology data is required to study the effect by the changing climate. These future climate data is generally prepared and downscaled from the GCMs, in which the monthly meteorological data are usually provided. We further constructed the future hourly climate data based on the identified GCM and studied the potential cooling energy variation of an actual office building
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