[HTML][HTML] Reinforcement learning for HVAC control in intelligent buildings: A technical and conceptual review
Abstract Heating, Ventilation and Air Conditioning (HVAC) systems in buildings are a major
source of global operational CO 2 emissions, primarily due to their high energy demands …
source of global operational CO 2 emissions, primarily due to their high energy demands …
[HTML][HTML] Towards data-driven and data-based control of wave energy systems: Classification, overview, and critical assessment
Currently, a significant effort in the world research panorama is focused on finding efficient
solutions to a carbon-free energy supply, wave energy being one of the most promising …
solutions to a carbon-free energy supply, wave energy being one of the most promising …
A safe reinforcement learning-based charging strategy for electric vehicles in residential microgrid
With the growing popularity of electric vehicles (EVs), it is a new challenge for the residential
microgrid system to conduct charging scheduling to meet the charging demands of EVs …
microgrid system to conduct charging scheduling to meet the charging demands of EVs …
[HTML][HTML] A new framework integrating reinforcement learning, a rule-based expert system, and decision tree analysis to improve building energy flexibility
This study presents a new framework that integrates machine learning and a domain
knowledge-based expert system to improve building energy flexibility. In this framework, a …
knowledge-based expert system to improve building energy flexibility. In this framework, a …
Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building
With the recent demand for decarbonization and energy efficiency, advanced HVAC control
using Deep Reinforcement Learning (DRL) becomes a promising solution. Due to its flexible …
using Deep Reinforcement Learning (DRL) becomes a promising solution. Due to its flexible …
[HTML][HTML] Effects of occupant thermostat preferences and override behavior on residential demand response in CityLearn
As space heating accounts for 54% of annual residential electricity consumption in Quebec,
demand response programs specifically target load shifting through the automated control of …
demand response programs specifically target load shifting through the automated control of …
Selective reinforcement graph mining approach for smart building energy and occupant comfort optimization
Optimizing Building energy consumption is a key solution to reducing their environmental
impact. In this context, Information Technology can be harnessed by deploying sensors …
impact. In this context, Information Technology can be harnessed by deploying sensors …
Improving the out-of-sample generalization ability of data-driven chiller performance models using physics-guided neural network
Modeling of the chiller performance is essential for the implementation of optimal energy-
efficient control strategies in a heating, ventilation, and air conditioning (HVAC) system …
efficient control strategies in a heating, ventilation, and air conditioning (HVAC) system …
Optimization of demand response-oriented electrolytic and fuel cell cogeneration system for community residents: uncovering flexibility and gaps
Low carbon energy systems are dependent on renewable power sources, which present
challenges in controllability compared to conventional sources. This poses difficulties in …
challenges in controllability compared to conventional sources. This poses difficulties in …
Optimal model-free adaptive control based on reinforcement Q-Learning for solar thermal collector fields
This study addresses the challenge and related difficulties of controlling solar collector fields
(SCFs) using high-complex models by proposing an adaptive optimal model-free controller …
(SCFs) using high-complex models by proposing an adaptive optimal model-free controller …