ED-DQN: An event-driven deep reinforcement learning control method for multi-zone residential buildings
出版年份 2023 全文链接
标题
ED-DQN: An event-driven deep reinforcement learning control method for multi-zone residential buildings
作者
关键词
-
出版物
BUILDING AND ENVIRONMENT
Volume -, Issue -, Pages 110546
出版商
Elsevier BV
发表日期
2023-06-20
DOI
10.1016/j.buildenv.2023.110546
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- A* guiding DQN algorithm for automated guided vehicle pathfinding problem of robotic mobile fulfillment systems
- (2023) Lei Luo et al. COMPUTERS & INDUSTRIAL ENGINEERING
- Event-driven online decoupling control mechanism for the variable flow rate HVAC system based on the medium response properties
- (2022) Jiaming Wang et al. BUILDING AND ENVIRONMENT
- Real-world implementation and cost of a cloud-based MPC retrofit for HVAC control systems in commercial buildings
- (2022) Max Bird et al. ENERGY AND BUILDINGS
- Optimal control method of HVAC based on multi-agent deep reinforcement learning
- (2022) Qiming Fu et al. ENERGY AND BUILDINGS
- The PID controller optimisation module using Fuzzy Self-Tuning PSO for Air Handling Unit in continuous operation
- (2022) Arkadiusz Ambroziak et al. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- A hierarchical optimal control strategy for continuous demand response of building HVAC systems to provide frequency regulation service to smart power grids
- (2021) Huilong Wang et al. ENERGY
- A Review of Deep Reinforcement Learning for Smart Building Energy Management
- (2021) Liang Yu et al. IEEE Internet of Things Journal
- Enhanced DQN Framework for Selecting Actions and Updating Replay Memory Considering Massive Non-Executable Actions
- (2021) Bonwoo Gu et al. Applied Sciences-Basel
- COVID-19 transmission in Mainland China is associated with temperature and humidity: A time-series analysis
- (2020) Hongchao Qi et al. SCIENCE OF THE TOTAL ENVIRONMENT
- DeepComfort: Energy-Efficient Thermal Comfort Control in Buildings Via Reinforcement Learning
- (2020) Guanyu Gao et al. IEEE Internet of Things Journal
- A clustering-based approach for “cross-scale” load prediction on building level in HVAC systems
- (2020) Wenqiang Li et al. APPLIED ENERGY
- Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning
- (2020) Yan Du et al. APPLIED ENERGY
- Energy optimization associated with thermal comfort and indoor air control via a deep reinforcement learning algorithm
- (2019) William Valladares et al. BUILDING AND ENVIRONMENT
- Effectiveness of the thermal mass of external walls on residential buildings for part-time part-space heating and cooling using the state-space method
- (2019) Jie Deng et al. ENERGY AND BUILDINGS
- Optimal control of HVAC and window systems for natural ventilation through reinforcement learning
- (2018) Yujiao Chen et al. ENERGY AND BUILDINGS
- Event-Based HVAC Control--A Complexity-Based Approach
- (2018) Qing-Shan Jia et al. IEEE Transactions on Automation Science and Engineering
- Automating occupant-building interaction via smart zoning of thermostatic loads: A switched self-tuning approach
- (2018) Simone Baldi et al. APPLIED ENERGY
- PMV-based event-triggered mechanism for building energy management under uncertainties
- (2017) Zhanbo Xu et al. ENERGY AND BUILDINGS
- Human-level control through deep reinforcement learning
- (2015) Volodymyr Mnih et al. NATURE
- Calibration of HVAC equipment PID coefficients for energy conservation
- (2011) A.P. Wemhoff ENERGY AND BUILDINGS
- DeST — An integrated building simulation toolkit Part I: Fundamentals
- (2008) Da Yan et al. Building Simulation
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