Digital twins for energy modelling of US buildings

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Digital models of the 129 million buildings across the US are now available for owners for energy management purposes.

The new tool was developed over five years at Oak Ridge National Laboratory (ORNL) using publicly available data to simulate the energy profile of every building in America.

This modelling programme, known as Automatic Building Energy Modeling (AutoBEM), is now publicly available. It is expected to give homeowners, utilities and companies a quick way to determine their energy use and cost-effective retrofits to improve energy efficiency and reduce carbon emissions.

“There are a lot of different industries that just don’t have the information they need to make actionable business decisions on how to improve energy efficiency. AutoBEM is a free resource that’s meant to grease the skids toward deployment,” says Joshua New, who led the research team at ORNL.

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As an example, he cites utility spending on energy efficiency and demand response programmes, which can be fine-tuned.

“Before this programme, no one had the capability to perform the analysis with detailed, building-specific energy modelling at this scale. Individual utilities now have the capability to perform modelling to show the potential of reducing demand and greenhouse gas emissions.”

The decarbonisation of buildings is key in moving towards net-zero targets. In the US buildings account for 40% of the nation’s energy consumption and 75% of its electricity.

Another group of potential users of the AutoBEM are urban planners, who could look at entire blocks and neighbourhoods to identify areas that have been historically overlooked in building improvement efforts.

AutoBEM accesses satellite imagery, street views and other publicly available data to gain insight into a building’s size and energy makeup, such as the number of windows, building envelope materials, number of floors; heating, ventilation and cooling systems and roof type.

The programme gathers those inputs using high-performance computing and creates a building energy model to predict which technologies could be deployed to save energy, including solar panels, heat pumps, smart thermostats or energy-efficient water heaters.

All internal characteristics are based on prototype buildings and standard building codes.