Application of two promising Reinforcement Learning algorithms for load shifting in a cooling supply system
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Application of two promising Reinforcement Learning algorithms for load shifting in a cooling supply system
Authors
Keywords
Reinforcement Learning, Load shifting, Optimal control, Thermal systems, Building automation and control, Simulation
Journal
ENERGY AND BUILDINGS
Volume 229, Issue -, Pages 110490
Publisher
Elsevier BV
Online
2020-09-20
DOI
10.1016/j.enbuild.2020.110490
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Reinforcement learning for building controls: The opportunities and challenges
- (2020) Zhe Wang et al. APPLIED ENERGY
- The ENTSO-E Transparency Platform – A review of Europe’s most ambitious electricity data platform
- (2018) Lion Hirth et al. APPLIED ENERGY
- Fuzzy Q-Learning for multi-agent decentralized energy management in microgrids
- (2018) P. Kofinas et al. APPLIED ENERGY
- Optimal control of HVAC and window systems for natural ventilation through reinforcement learning
- (2018) Yujiao Chen et al. ENERGY AND BUILDINGS
- Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice
- (2018) F. Ruelens et al. IEEE Transactions on Smart Grid
- Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities
- (2018) José R. Vázquez-Canteli et al. Sustainable Cities and Society
- Reinforcement learning for demand response: A review of algorithms and modeling techniques
- (2018) José R. Vázquez-Canteli et al. APPLIED ENERGY
- Demand-Side Management of Domestic Electric Water Heaters Using Approximate Dynamic Programming
- (2017) Khalid Al-jabery et al. IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
- Mastering the game of Go without human knowledge
- (2017) David Silver et al. NATURE
- An Online Learning Algorithm for Demand Response in Smart Grid
- (2017) Shahab Bahrami et al. IEEE Transactions on Smart Grid
- A Long-Short Term Memory Recurrent Neural Network Based Reinforcement Learning Controller for Office Heating Ventilation and Air Conditioning Systems
- (2017) et al. Processes
- Reinforcement learning for optimal control of low exergy buildings
- (2015) Lei Yang et al. APPLIED ENERGY
- Human-level control through deep reinforcement learning
- (2015) Volodymyr Mnih et al. NATURE
- Reinforcement Learning of Heuristic EV Fleet Charging in a Day-Ahead Electricity Market
- (2015) Stijn Vandael et al. IEEE Transactions on Smart Grid
- Theory and applications of HVAC control systems – A review of model predictive control (MPC)
- (2013) Abdul Afram et al. BUILDING AND ENVIRONMENT
- Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads
- (2011) Peter Palensky et al. IEEE Transactions on Industrial Informatics
- Design and implementation of smart home energy management systems based on zigbee
- (2010) Dae-Man Han et al. IEEE TRANSACTIONS ON CONSUMER ELECTRONICS
Become a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get StartedAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started