Better building energy modelling using microclimate data

Building Energy Modelling (BEM) is a tool that plays a significant role in forecasting future energy demand from the building sector and for improving urban resilience in a changing environment. However, the commonly used weather data in BEM is usually from a typical meteorological year, generated based on historical records. This data might be incapable of representing the current and future weather conditions in the context of climate change and urbanisation.

Prof. Yuan Chao and his team analyse the sensitivity of BEM performance by using different weather datasets, based on urban microclimates in Singapore. Then, they validate the accuracy of the BEM with its generated microclimate dataset by examining the actual energy consumption in seven HDB buildings in Singapore. The prediction errors in monthly energy consumption are reduced from 12% to 6%, highlighting how the microclimate data is more accurate than typical meteorological year data for BEM calculation.

Applying the new microclimate data framework, the FRS team draws possible predictions of the residential energy demands under the Covid-19 pandemic and climate change scenarios for climate resilience implementation. It is revealed that, compared to the normal pre-pandemic situation, the monthly energy consumption will increase by 24% in the event of a Covid-19 pandemic scenario, and increase by 6% under an extreme climate change scenario.

Their work, “external page Better Understanding on Impact of Microclimate Information on Building Energy Modelling Performance for Urban Resilience”, has been published in Sustainable Cities and Society (2022).