BEdotNINJA

building energy.ninja

by the Energy in Cities group, University of Victoria

Providing instant building simulations using surrogate models.

Machine learning surrogate models were trained on EnergyPlus.

They provide accurate simulation output estimates of the building heating and cooling load profiles (hourly) for any location in the world.

Two building types (office, residential) are available with 13 parameters to customize the design.

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By following the steps below, this tool simulates the heating demand or cooling demand of a user-specified building through the use of a deep temporal convolutional network as a building energy surrogate model.

Step 1: Choose the building type you would like to simulate.

Step 2: Pick a location on the map to run the simulation for.

Step 3: Customize the building design and download the heating and cooling load.

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Victoria, mediterranean climate

Edmonton, humid continental climate

Ottawa, warm summer continental climate

Locations on this map are coloured according to their Heading Degree Day.

CommercialResidential

Location:

Heating Degree Day:

North Axis
U-value [W/(mK)]
Wall insulation thickness [m]
Window transmittance
Window conductivity [W/(mK)]
Wall solar absorptance
Heating setpoint [C]
Cooling setpoint [C]
Equipment Gains [W/m2]
Light Gains [W/m2]
Ventilation rate [m3/(s*Person)]
Infiltration rate [m3/(s*m2)]
Window-to-wall ratio

Related research works:

Click to read article

Citation:

Westermann, Paul, Matthias Welzel, and Ralph Evins. "Using a deep temporal convolutional network as a building energy surrogate model that spans multiple climate zones." Applied Energy 278 (2020): 115563.

Westermann, Paul, and Ralph Evins. "Surrogate modelling for sustainable building design–A review." Energy and Buildings 198 (2019): 170-186.

Westermann, Paul W. Advancing surrogate modelling for sustainable building design. Diss. 2020. http://hdl.handle.net/1828/12127