A review of physics-informed machine learning for building energy modeling
Building energy modeling (BEM) refers to computational modeling of building energy use
and indoor dynamics. As a critical component in sustainable and resilient building …
and indoor dynamics. As a critical component in sustainable and resilient building …
[HTML][HTML] Towards scalable physically consistent neural networks: An application to data-driven multi-zone thermal building models
With more and more data being collected, data-driven modeling methods have been gaining
in popularity in recent years. While physically sound, classical gray-box models are often …
in popularity in recent years. While physically sound, classical gray-box models are often …
Simba: System identification methods leveraging backpropagation
This manuscript details and extends the system identification methods leveraging the
backpropagation (SIMBa) toolbox presented in previous work, which uses well-established …
backpropagation (SIMBa) toolbox presented in previous work, which uses well-established …
Physics-informed neural network modeling and predictive control of district heating systems
LB de Giuli, A La Bella… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
This article addresses the data-based modeling and optimal control of district heating
systems (DHSs). Physical models of such large-scale networked systems are governed by …
systems (DHSs). Physical models of such large-scale networked systems are governed by …
Stable linear subspace identification: A machine learning approach
Machine Learning (ML) and linear System Identification (SI) have been historically
developed independently. In this paper, we leverage well-established ML tools—especially …
developed independently. In this paper, we leverage well-established ML tools—especially …
[HTML][HTML] Neural differential equations for temperature control in buildings under demand response programs
Abstract Heating Ventilation and Air Conditioning (HVAC) are energy-intensive systems that
greatly contribute to peak demand, which can cause stability and reliability issues in the grid …
greatly contribute to peak demand, which can cause stability and reliability issues in the grid …
Unconstrained learning of networked nonlinear systems via free parametrization of stable interconnected operators
This paper characterizes a new parametrization of nonlinear networked incrementally L_2-
bounded operators in discrete time. The distinctive novelty is that our parametrization is free …
bounded operators in discrete time. The distinctive novelty is that our parametrization is free …
Universal approximation property of Hamiltonian deep neural networks
M Zakwan, M d'Angelo… - IEEE Control Systems …, 2023 - ieeexplore.ieee.org
This letter investigates the universal approximation capabilities of Hamiltonian Deep Neural
Networks (HDNNs) that arise from the discretization of Hamiltonian Neural Ordinary …
Networks (HDNNs) that arise from the discretization of Hamiltonian Neural Ordinary …
Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach
M Zakwan, G Ferrari-Trecate - arXiv preprint arXiv:2411.10096, 2024 - arxiv.org
The control of large-scale cyber-physical systems requires optimal distributed policies
relying solely on limited communication with neighboring agents. However, computing …
relying solely on limited communication with neighboring agents. However, computing …
Neural Distributed Controllers with Port-Hamiltonian Structures
M Zakwan, G Ferrari-Trecate - arXiv preprint arXiv:2403.17785, 2024 - arxiv.org
Controlling large-scale cyber-physical systems necessitates optimal distributed policies,
relying solely on local real-time data and limited communication with neighboring agents …
relying solely on local real-time data and limited communication with neighboring agents …