Data-driven building energy efficiency prediction based on envelope heat
  losses using physics-informed neural networks
Vasilis Michalakopoulos Sotiris Pelekis Giorgos Kormpakis Vagelis Karakolis Spiros Mouzakitis Dimitris Askounis
The analytical prediction of building energy performance in residential
buildings based on the heat losses of its individual envelope components is a
challenging task. It is worth noting that this field is still in its infancy,
with relatively limited research conducted in this specific area to date,
especially when it comes for data-driven approaches. In this paper we introduce
a novel physics-informed neural network model for addressing this problem.
Through the employment of unexposed datasets that encompass general building
information, audited characteristics, and heating energy consumption, we feed
the deep learning model with general building information, while the model's
output consists of the structural components and several thermal properties
that are in fact the basic elements of an energy performance certificate (EPC).
On top of this neural network, a function, based on physics equations,
calculates the energy consumption of the building based on heat losses and
enhances the loss function of the deep learning model. This methodology is
tested on a real case study for 256 buildings located in Riga, Latvia. Our
investigation comes up with promising results in terms of prediction accuracy,
paving the way for automated, and data-driven energy efficiency performance
prediction based on basic properties of the building, contrary to exhaustive
energy efficiency audits led by humans, which are the current status quo.