State of the art review on model predictive control (MPC) in Heating Ventilation and Air-conditioning (HVAC) field
Y Yao, DK Shekhar - Building and Environment, 2021 - Elsevier
Building systems are subject of dynamic system that have a general feature of non-linearity
and in turn, present us with different challenges for its optimized control of energy-saving …
and in turn, present us with different challenges for its optimized control of energy-saving …
[HTML][HTML] A review of reinforcement learning for controlling building energy systems from a computer science perspective
D Weinberg, Q Wang, TO Timoudas… - Sustainable cities and …, 2023 - Elsevier
Energy efficient control of energy systems in buildings is a widely recognized challenge due
to the use of low temperature heating, renewable electricity sources, and the incorporation of …
to the use of low temperature heating, renewable electricity sources, and the incorporation of …
Towards optimal HVAC control in non-stationary building environments combining active change detection and deep reinforcement learning
X Deng, Y Zhang, H Qi - Building and environment, 2022 - Elsevier
Energy consumption for heating, ventilation and air conditioning (HVAC) has increased
significantly and accounted for a large proportion of building energy growth. Advanced …
significantly and accounted for a large proportion of building energy growth. Advanced …
[HTML][HTML] Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control
Controlling heating, ventilation and air-conditioning (HVAC) systems is crucial to improving
demand-side energy efficiency. At the same time, the thermodynamics of buildings and …
demand-side energy efficiency. At the same time, the thermodynamics of buildings and …
Reinforcement learning of occupant behavior model for cross-building transfer learning to various HVAC control systems
Occupant behavior plays an important role in the evaluation of building performance.
However, many contextual factors, such as occupancy, mechanical system and interior …
However, many contextual factors, such as occupancy, mechanical system and interior …
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 …
Benchmarking high performance HVAC Rule-Based controls with advanced intelligent Controllers: A case study in a Multi-Zone system in Modelica
The design, commissioning, and retrofit of heating, ventilation, and air-conditioning (HVAC)
control systems are crucially important for energy efficiency. However, designers and control …
control systems are crucially important for energy efficiency. However, designers and control …
Deepapp: a deep reinforcement learning framework for mobile application usage prediction
This paper aims to predict the apps a user will open on her mobile device next. Such an
information is essential for many smartphone operations, eg, app pre-loading and content …
information is essential for many smartphone operations, eg, app pre-loading and content …
Sinergym: a building simulation and control framework for training reinforcement learning agents
J Jiménez-Raboso, A Campoy-Nieves… - Proceedings of the 8th …, 2021 - dl.acm.org
We introduce Sinergym, an open-source building simulation and control framework for
training reinforcement learning agents. The proposed framework is compatible with …
training reinforcement learning agents. The proposed framework is compatible with …
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 …