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