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 …
[HTML][HTML] Physically consistent neural networks for building thermal modeling: theory and analysis
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 …
energy transition. Building models are ubiquitous since they are needed at each stage of the …
Physics informed neural networks for control oriented thermal modeling of buildings
Buildings constitute more than 40% of total primary energy consumption worldwide and are
bound to play an important role in the energy transition process. To unlock their potential, we …
bound to play an important role in the energy transition process. To unlock their potential, we …
[HTML][HTML] Evaluation of advanced control strategies for building energy systems
Advanced building control strategies like model predictive control and reinforcement
learning can consider forecasts for weather, occupancy, and energy prices. Combined with …
learning can consider forecasts for weather, occupancy, and energy prices. Combined with …
Field test of Model Predictive Control in residential buildings for utility cost savings
Air conditioners in residential buildings often rely on local feedback control to regulate the
indoor temperature, where the unit is cycled on and off by comparing the actual temperature …
indoor temperature, where the unit is cycled on and off by comparing the actual temperature …
Energy optimization algorithms for multi-residential buildings: A model predictive control application
This study presents an optimization algorithm for Model Predictive Control (MPC) of the
HVAC systems in multi-family residential buildings assessing the performance of four …
HVAC systems in multi-family residential buildings assessing the performance of four …
Physics-informed machine learning for modeling and control of dynamical systems
Physics-informed machine learning (PIML) is a set of methods and tools that systematically
integrate machine learning (ML) algorithms with physical constraints and abstract …
integrate machine learning (ML) algorithms with physical constraints and abstract …
[HTML][HTML] Safe operation of online learning data driven model predictive control of building energy systems
Abstract Model predictive control is a promising approach to reduce the CO 2 emissions in
the building sector. However, the vast modeling effort hampers the widescale practical …
the building sector. However, the vast modeling effort hampers the widescale practical …
Sharing is caring: An extensive analysis of parameter-based transfer learning for the prediction of building thermal dynamics
In recent years deep neural networks have been proposed as a lightweight data-driven
model to capture high-dimensional, nonlinear physical processes to predict building thermal …
model to capture high-dimensional, nonlinear physical processes to predict building thermal …