A systematic review of reinforcement learning application in building energy-related occupant behavior simulation
H Yu, VWY Tam, X Xu - Energy and Buildings, 2024 - Elsevier
The building and construction industry has consistently been a major contributor to energy
consumption and carbon emissions. With stochastic interactions between occupants and …
consumption and carbon emissions. With stochastic interactions between occupants and …
Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings
In recent years, advanced control strategies based on Deep Reinforcement Learning (DRL)
proved to be effective in optimizing the management of integrated energy systems in …
proved to be effective in optimizing the management of integrated energy systems in …
Prospects and challenges of reinforcement learning-based HVAC control
Increasing worldwide energy demand and the resulting escalations in greenhouse gas
emissions require a reassessment of energy usage in many sectors. The building industry …
emissions require a reassessment of energy usage in many sectors. The building industry …
Multi-source transfer learning method for enhancing the deployment of deep reinforcement learning in multi-zone building HVAC control
Deep reinforcement learning (DRL) control methods have shown great potential for optimal
HVAC control, but they require significant time and data to learn effective policies. By …
HVAC control, but they require significant time and data to learn effective policies. By …
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 …
in 2020, they are one of the core targets for energy-efficiency research and regulations …
Mitigating an adoption barrier of reinforcement learning-based control strategies in buildings
Reinforcement learning (RL) algorithms have shown great promise in controlling building
systems to minimize energy use, operational cost, and occupant discomfort. RL agents learn …
systems to minimize energy use, operational cost, and occupant discomfort. RL agents learn …
An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy …
Abstract Deep Reinforcement Learning (DRL)-based control shows enhanced performance
in the management of integrated energy systems when compared with Rule-Based …
in the management of integrated energy systems when compared with Rule-Based …
Reinforcement learning for occupant behavior modeling in public buildings: Why, what and how?
H Yu, X Xu - Journal of Building Engineering, 2024 - Elsevier
Effective control of energy consumption in public buildings holds paramount significance for
global sustainable development. However, uncertainty in occupant behavior during …
global sustainable development. However, uncertainty in occupant behavior during …
Novel machine learning paradigms-enabled methods for smart building operations in data-challenging contexts: Progress and perspectives
The increasing availability of building operational data has greatly encouraged the
development of advanced data-driven technologies for smart building operations. Building …
development of advanced data-driven technologies for smart building operations. Building …
[PDF][PDF] House Energy Management System, for balancing Electricity Costs and Residential Comfort, based on Deep Reinforcement Learning
Smart homes are becoming increasingly popular for their potential to reduce electricity costs,
through device optimization. Balancing residential comfort with electricity cost reduction …
through device optimization. Balancing residential comfort with electricity cost reduction …