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The analytical prediction of building energy performance in residentialbuildings based on the heat losses of its individual envelope components is achallenging 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 introducea novel physics-informed neural network model for addressing this problem.Through the employment of unexposed datasets that encompass general buildinginformation, audited characteristics, and heating energy consumption, we feedthe deep learning model with general building information, while the model'soutput consists of the structural components and several thermal propertiesthat 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 andenhances the loss function of the deep learning model. This methodology istested on a real case study for 256 buildings located in Riga, Latvia. Ourinvestigation comes up with promising results in terms of prediction accuracy,paving the way for automated, and data-driven energy efficiency performanceprediction based on basic properties of the building, contrary to exhaustiveenergy efficiency audits led by humans, which are the current status quo.

Data-driven building energy efficiency prediction based on envelope heat losses using physics-informed neural networks