Hydrogen jet and diffusion modeling by physics-informed graph neural network
Abstract Renewable Power-to-Hydrogen (P2H2) system is an emerging decarbonization
strategy for achieving global carbon neutrality. However, the propensity of hydrogen to leak …
strategy for achieving global carbon neutrality. However, the propensity of hydrogen to leak …
A review on hybrid physics and data-driven modeling methods applied in air source heat pump systems for energy efficiency improvement
Purely data-driven modeling methods exhibit inherent “black box” characteristics when
applied to the air source heat pump (ASHP) systems for energy efficiency improvement …
applied to the air source heat pump (ASHP) systems for energy efficiency improvement …
Long-term experimental evaluation and comparison of advanced controls for HVAC systems
The tremendous energy usage from buildings leads to research studies on their
improvement, among which advanced building control plays an important role. In advanced …
improvement, among which advanced building control plays an important role. In advanced …
Modularized neural network incorporating physical priors for future building energy modeling
Building energy modeling (BEM) is fundamental for achieving optimized energy control,
resilient retrofit designs, and sustainable urbanization to mitigate climate change. However …
resilient retrofit designs, and sustainable urbanization to mitigate climate change. However …
[HTML][HTML] Hierarchical MPC for building energy management: Incorporating data-driven error compensation and mitigating information asymmetry
The increasing adoption of renewable energy sources (RESs) in public power grids has led
to a demand for more intelligent energy management systems (EMSs) in large-scale …
to a demand for more intelligent energy management systems (EMSs) in large-scale …
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] Data-driven adaptive building thermal controller tuning with constraints: A primal–dual contextual Bayesian optimization approach
We study the problem of tuning the parameters of a room temperature controller to minimize
its energy consumption, subject to the constraint that the daily cumulative thermal discomfort …
its energy consumption, subject to the constraint that the daily cumulative thermal discomfort …
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 …
[HTML][HTML] Energy flexibility quantification of a tropical net-zero office building using physically consistent neural network-based model predictive control
Building energy flexibility plays a critical role in demand-side management for reducing
utility costs for building owners and sustainable, reliable, and smart grids. Realizing building …
utility costs for building owners and sustainable, reliable, and smart grids. Realizing building …
Physics-consistent input convex neural network-driven reinforcement learning control for multi-zone radiant ceiling heating and cooling systems: An experimental …
Radiant ceiling heating and cooling system is a technology used for space heating and
cooling. Owing to the variable weather conditions, occupant behavior, and thermal lag of the …
cooling. Owing to the variable weather conditions, occupant behavior, and thermal lag of the …