[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 …
Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization
Being a primary contributor to global energy consumption and energy-related carbon
emissions, the building and building construction sectors are a crucial player in the …
emissions, the building and building construction sectors are a crucial player in the …
Control of Multicarrier Energy Systems from Buildings to Networks
Cost, efficiency, and emissions concerns have motivated the application of advanced control
techniques to multiple carrier energy systems. Research in energy management and control …
techniques to multiple carrier energy systems. Research in energy management and control …
[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 …
[HTML][HTML] Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC
Because physics-based building models are difficult to obtain as each building is individual,
there is an increasing interest in generating models suitable for building MPC directly from …
there is an increasing interest in generating models suitable for building MPC directly from …
Building energy management with reinforcement learning and model predictive control: A survey
Building energy management has been recognized as of significant importance on
improving the overall system efficiency and reducing the greenhouse gas emission …
improving the overall system efficiency and reducing the greenhouse gas emission …
Variational Onsager Neural Networks (VONNs): A thermodynamics-based variational learning strategy for non-equilibrium PDEs
We propose a thermodynamics-based learning strategy for non-equilibrium evolution
equations based on Onsager's variational principle, which allows us to write such PDEs in …
equations based on Onsager's variational principle, which allows us to write such PDEs in …
[HTML][HTML] Identifying the validity domain of machine learning models in building energy systems
The building sector significantly contributes to climate change. To improve its carbon
footprint, applications like model predictive control and predictive maintenance rely on …
footprint, applications like model predictive control and predictive maintenance rely on …
[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 …
Real-time machine-learning-based optimization using input convex long short-term memory network
Neural network-based optimization and control methods, often referred to as black-box
approaches, are increasingly gaining attention in energy and manufacturing systems …
approaches, are increasingly gaining attention in energy and manufacturing systems …