Better understanding on impact of microclimate information on building energy modelling performance for urban resilience. (May 2022)

• Record Type: Journal Article
• Authors: Xu, Lei; Tong, Shanshan; He, Wenhui; Zhu, Wei; Mei, Shuojun; Cao, Kai; Yuan, Chao
• Is Part Of: Sustainable cities and society. Volume 80(2022)
• Publication Date: 2022-05
• Subjects: Building energy modelling -- Weather data -- EnergyPlus -- Anthropogenic heat, Urban resilience; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2022.103775
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: New framework is developed and implemented to provide a representative microclimate dataset for BEM. Significant improvement on BEM performance is achieved using the microclimate dataset from the developed framework. Sensitivity of building energy performance with various climate datasets is tested. Building energy use modelled by new data matches with actual energy consumption. Residential energy use under COVID and global warming scenarios are predicted. Abstract: Building Energy Modelling (BEM) plays a significant role in projecting future building energy demands and predicting urban climate resilience in the context of climate change and urbanization. Accurate weather data are important components in BEM. In this study, we investigate how the BEM performance is affected by weather datasets, including 1) the typical meteorological year (TMY) data, 2) data measured at the suburban ground, and 3) three microclimate datasets, i.e., data measured at a high-rise rooftop near the site, data measured at the near-ground open space close to the site, and developed microclimate data within the urban canopy layer at the site. The new microclimate data are developed by integrating near-ground measured data and microclimate modelling results using a practical GIS model. Compared with the actual energy usage, the predictions of BEM using the developed microclimate data show the least mean bias error of 6%, while the error is 12% when TMY data are used. We further utilize this method to develop microclimate datasets and predict residential energy consumptions under the short-term coronavirus pandemic and long-term climate change scenarios. The findings provide scientific support for the decision-making in future energy planning to improve urban climate resilience.