[HTML][HTML] Interpretable machine learning for building energy management: A state-of-the-art review
Abstract Machine learning has been widely adopted for improving building energy efficiency
and flexibility in the past decade owing to the ever-increasing availability of massive building …
and flexibility in the past decade owing to the ever-increasing availability of massive building …
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 …
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 …
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 …
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 …
Physics-constrained cooperative learning-based reference models for smart management of chillers considering extrapolation scenarios
Smart management of building energy devices, including their optimal control and fault
detection technology, is of great significance to building energy conservation. The core of …
detection technology, is of great significance to building energy conservation. The core of …
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 …
Deep reinforcement learning towards real-world dynamic thermal management of data centers
Abstract Deep Reinforcement Learning has been increasingly researched for Dynamic
Thermal Management in Data Centers. However, existing works typically evaluate the …
Thermal Management in Data Centers. However, existing works typically evaluate the …
[HTML][HTML] Experimental data-driven model predictive control of a hospital HVAC system during regular use
ET Maddalena, SA Mueller, RM dos Santos… - Energy and …, 2022 - Elsevier
Herein we report a multi-zone, heating, ventilation and air-conditioning (HVAC) control case
study of an industrial plant responsible for cooling a hospital surgery center. The adopted …
study of an industrial plant responsible for cooling a hospital surgery center. The adopted …
[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 …