 Physically Consistent Neural Networks for building thermal modeling: Theory and analysisL. Di Natale, B. Svetozarevic, P. Heer and C.N. Jones Applied Energy, 2022, vol. 325, issue C, No S0306261922010819 Abstract: Due to their high energy intensity, buildings play a major role in the current worldwide energy transition. Building models are ubiquitous since they are needed at each stage of the life of buildings, i.e. for design, retrofitting, and control operations. Classical white-box models, based on physical equations, are bound to follow the laws of physics but the specific design of their underlying structure might hinder their expressiveness and hence their accuracy. On the other hand, black-box models are better suited to capture nonlinear building dynamics and thus can often achieve better accuracy, but they require a lot of data and might not follow the laws of physics, a problem that is particularly common for neural network (NN) models. To counter this known generalization issue, physics-informed NNs have recently been introduced, where researchers introduce prior knowledge in the structure of NNs to ground them in known underlying physical laws and avoid classical NN generalization issues. Keywords: Neural Networks; Physical consistency; Prior knowledge; Building models; Deep Learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:325:y:2022:i:c:s0306261922010819 Ordering information: This journal article can be ordered from DOI: 10.1016/j.apenergy.2022.119806 Access Statistics for this article Applied Energy is currently edited by J. Yan More articles in Applied Energy from Elsevier |
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