Wietzke T, Graichen K (2025)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2025
Book Volume: 59
Pages Range: 43-48
Conference Proceedings Title: IFAC-PapersOnLine
Event location: Vienna (Austria)
URI: https://www.sciencedirect.com/science/article/pii/S2405896325002265
DOI: 10.1016/j.ifacol.2025.03.009
Open Access Link: https://www.sciencedirect.com/science/article/pii/S2405896325002265
Efficient energy management in building energy systems (BES) is essential for
reducing energy consumption while maintaining thermal comfort. One effective approach is
Model Predictive Control (MPC), which optimizes control actions based on a model of the
building; however, deriving such models can be costly and time-consuming. This paper combines
Gaussian Processes (GP) with parametric mean functions which can be viewed as Physics
Informed Gaussian Processes (PIGP). The PIGP is evaluated against other approaches to
identify the thermal dynamics of BES, showing that the PIGP provides the best predictive
accuracy. Furthermore, these models are integrated into a nonlinear MPC to compare energy
demand and constraint violations in a sample BES, with simulations indicating that the PIGP
results in lower energy demand.
APA:
Wietzke, T., & Graichen, K. (2025). Physics-informed sparse Gaussian processes for model predictive control in building energy systems. In IFAC-PapersOnLine (pp. 43-48). Vienna (Austria).
MLA:
Wietzke, Thore, and Knut Graichen. "Physics-informed sparse Gaussian processes for model predictive control in building energy systems." Proceedings of the 11th Vienna International Conference on Mathematical Modelling (MATHMOD 25), Vienna (Austria) 2025. 43-48.
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