Trade-off decisions in a novel deep reinforcement learning for energy savings in HVAC systems-Reference-Cited by-同舟云学术

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Trade-off decisions in a novel deep reinforcement learning for energy savings in HVAC systems

Published:2022-08-04 Issue:6 Volume:15 Page:809-831
ISSN:1940-1493
Container-title:Journal of Building Performance Simulation
language:en
Short-container-title:Journal of Building Performance Simulation

Author:

Dawood Suroor M.¹²,Hatami Alireza¹^ORCID,Homod Raad Z.³^ORCID

Affiliation:

1. Dept. of Elec. Eng., Bu-Ali Sina University, Mahdieh Street, Hamedan 65178-38695, Iran

2. Dept. of Chem. Eng. and Oil Refining, Basrah University for Oil and Gas, Basrah 61004, Iraq

3. Dept. of Oil and Gas Eng., Basrah University for Oil and Gas, Basrah 61004, Iraq

Publisher

Informa UK Limited

Subject

Computer Science Applications,Modeling and Simulation,Building and Construction,Architecture

Link

https://www.tandfonline.com/doi/pdf/10.1080/19401493.2022.2099465

Reference51 articles.

1. Application of deep Q-networks for model-free optimal control balancing between different HVAC systems

2. A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings

3. Reinforcement learning for whole-building HVAC control and demand response

4. User comfort and energy efficiency in HVAC systems by Q-learning

5. The nonlinear autoregressive network with exogenous inputs (NARX) neural network to damp power system oscillations

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1. Reinforcement learning for occupant behavior modeling in public buildings: Why, what and how?;Journal of Building Engineering;2024-11

2. Reinforcement learning for HVAC control in intelligent buildings: A technical and conceptual review;Journal of Building Engineering;2024-10

3. Building simulation in adaptive training of machine learning models;Automation in Construction;2024-09

4. Study on indoor temperature optimal control of air-conditioning based on Twin Delayed Deep Deterministic policy gradient algorithm;Energy and Buildings;2024-08

5. Application of energy combined thermal comfort in intelligent building management in complex environments;Energy Informatics;2024-07-03

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