Data-driven building energy efficiency prediction using physics-informed neural networks

arXiv:2311.08035v2 Announce Type: replace
Abstract: 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 …

abstract arxiv building buildings components cs.ai cs.ce cs.lg data data-driven efficiency energy energy efficiency heat losses networks neural networks performance physics physics-informed prediction research type