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 …
A review of reinforcement learning applications to control of heating, ventilation and air conditioning systems
Reinforcement learning has emerged as a potentially disruptive technology for control and
optimization of HVAC systems. A reinforcement learning agent takes actions, which can be …
optimization of HVAC systems. A reinforcement learning agent takes actions, which can be …
Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future Directions
IA Kachalla, C Ghiaus - Energies, 2024 - mdpi.com
Accurate and efficient prediction of electric water boiler (EWB) energy consumption is
significant for energy management, effective demand response, cost minimisation, and …
significant for energy management, effective demand response, cost minimisation, and …
Electric water heater modeling for large-scale distribution power systems studies with energy storage CTA-2045 based VPP and CVR
As the smart grid involves more new technologies such as electric vehicles (EVs) and
distributed energy resources (DERs), more attention is needed in research to general …
distributed energy resources (DERs), more attention is needed in research to general …
Deep forest-based DQN for cooling water system energy saving control in HVAC
Z Han, Q Fu, J Chen, Y Wang, Y Lu, H Wu, H Gui - Buildings, 2022 - mdpi.com
Currently, reinforcement learning (RL) has shown great potential in energy saving in HVAC
systems. However, in most cases, RL takes a relatively long period to explore the …
systems. However, in most cases, RL takes a relatively long period to explore the …
Multi-objective deep reinforcement learning for a water heating system with solar energy and heat recovery
A Riebel, JM Cardemil, E López - Energy, 2024 - Elsevier
Deep reinforcement learning (DRL) has gained attention from the scientific community due
to its potential for optimizing complex control schemes. This study describes the …
to its potential for optimizing complex control schemes. This study describes the …
Deep reinforcement learning with online data augmentation to improve sample efficiency for intelligent HVAC control
Deep Reinforcement Learning (DRL) has started showing success in real-world applications
such as building energy optimization. Much of the research in this space utilized simulated …
such as building energy optimization. Much of the research in this space utilized simulated …
Reinforcement-learning-based Smart Water Heater Control: An Actual Deployment
Utilizing smart control algorithms for electric water heaters (EWHs) is essential for fully
harnessing the demand response (DR) potential of EWHs. For this reason, the use of …
harnessing the demand response (DR) potential of EWHs. For this reason, the use of …
Интеллектуальные мультиагентные системы в электроэнергетике
АИ Хальясмаа, СА Ерошенко, ИФ Юманова… - Новосибирск …, 2023 - elibrary.ru
В монографии рассматриваются подходы к созданию мультиагентных систем и их
применению в электроэнергетике, описываются архитектуры и классификации таких …
применению в электроэнергетике, описываются архитектуры и классификации таких …
Reinforcement learning for watershed and aquifer management: a nationwide view in the country of Mexico with emphasis in Baja California Sur
Reinforcement Learning (RL) is a method that teaches agents to make informed decisions in
diverse environments through trial and error, aiming to maximize a reward function and …
diverse environments through trial and error, aiming to maximize a reward function and …