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

Autor: Michalakopoulos, Vasilis, Pelekis, Sotiris, Kormpakis, Giorgos, Karakolis, Vagelis, Mouzakitis, Spiros, Askounis, Dimitris

Publikováno v: 2024 IEEE Conference on Technologies for Sustainability (SusTech)

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 limite

Externí odkaz: http://arxiv.org/abs/2311.08035

Leveraging extreme scale analytics, AI and digital twins for maritime digitalization: the VesselAI architecture.

Autor: Ilias L; Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece., Tsapelas G; Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece., Kapsalis P; Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece., Michalakopoulos V; Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece., Kormpakis G; Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece., Mouzakitis S; Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece., Askounis D; Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.

Publikováno v: Frontiers in big data [Front Big Data] 2023 Jul 27; Vol. 6, pp. 1220348. Date of Electronic Publication: 2023 Jul 27 (Print Publication: 2023).