[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 …

Systematic review on deep reinforcement learning-based energy management for different building types

A Shaqour, A Hagishima - Energies, 2022 - mdpi.com
Owing to the high energy demand of buildings, which accounted for 36% of the global share
in 2020, they are one of the core targets for energy-efficiency research and regulations …

Energy-efficient control of thermal comfort in multi-zone residential HVAC via reinforcement learning

ZK Ding, QM Fu, JP Chen, HJ Wu, Y Lu… - Connection Science, 2022 - Taylor & Francis
Energy efficient control of thermal comfort has been already an important part of residential
heating, ventilation, and air conditioning (HVAC) systems. However, the optimisation of …

Occupant-oriented economic model predictive control for demand response in buildings

M Frahm, P Zwickel, J Wachter, F Langner… - Proceedings of the …, 2022 - dl.acm.org
The present paper develops an Economic Model Predictive Control (EMPC) framework to
provide Demand-Response (DR) for supporting the power grid stability while also …

Real-time energy management in smart homes through deep reinforcement learning

J Aldahmashi, X Ma - IEEE Access, 2024 - ieeexplore.ieee.org
In light of the growing prevalence of distributed energy resources, energy storage systems
(ESs), and electric vehicles (EVs) at the residential scale, home energy management (HEM) …

Analysis of Building Model Forecasts using Autonomous HVAC Optimization System for Residential Neighborhood

V Lebakula, H Zandi, C Winstead… - 2023 IEEE Energy …, 2023 - ieeexplore.ieee.org
Heating, ventilation, and air conditioning (HVAC) systems account for the highest share of
home energy consumption in the United States. Optimized HVAC control can provide …

[PDF][PDF] Review and Evaluation of Multi-Agent Control Applications for Energy Management in Buildings.

P Michailidis, I Michailidis… - Energies (19961073 …, 2024 - researchgate.net
The current paper presents a comprehensive review analysis of Multi-agent control
methodologies for Integrated Building Energy Management Systems (IBEMSs), considering …

MAQMC: Multi-Agent Deep Q-Network for Multi-Zone Residential HVAC Control.

Z Ding, Q Fu, J Chen, Y Lu, H Wu… - … in Engineering & …, 2023 - search.ebscohost.com
The optimization of multi-zone residential heating, ventilation, and air conditioning (HVAC)
control is not an easy task due to its complex dynamic thermal model and the uncertainty of …

Physics-Informed Data Denoising for Real-Life Sensing Systems

X Zhang, X Fu, D Teng, C Dong… - Proceedings of the 21st …, 2023 - dl.acm.org
Sensors measuring real-life physical processes are ubiquitous in today's interconnected
world. These sensors inherently bear noise that often adversely affects the performance and …

Predictive AC Control Using Deep Learning: Improving Comfort and Energy Saving

AA bin Mohd Ameeruddin, WN Tan, MT Gan… - … : International Journal on …, 2023 - joiv.org
The growing global population and the availability of energy-hungry smart devices are
critical factors in today's alarmingly high electricity usage. The majority of energy used in …