Deep Reinforcement Learning for Joint Datacenter and HVAC Load Control in Distributed Mixed-Use Buildings-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning for Joint Datacenter and HVAC Load Control in Distributed Mixed-Use Buildings

Published:2021-07-01 Issue:3 Volume:6 Page:370-384
ISSN:2377-3782
Container-title:IEEE Transactions on Sustainable Computing
language:
Short-container-title:IEEE Trans. Sustain. Comput.

Author:

Wei Tianshu^ORCID,Ren Shaolei^ORCID,Zhu Qi^ORCID

Funder

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Control and Optimization,Hardware and Architecture,Renewable Energy, Sustainability and the Environment,Software

Link

https://ieeexplore.ieee.org/ielam/7274860/9531050/8691496-aam.pdf

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2. Driving Energy Efficiency at Scale by Mass Deployment of AI based Chiller Energy Optimization;2024 the 8th International Conference on Innovation in Artificial Intelligence;2024-03-16

3. Energy optimization for HVAC systems in multi-VAV open offices: A deep reinforcement learning approach;Applied Energy;2024-02

4. Distributed LSTM-GCN-Based Spatial–Temporal Indoor Temperature Prediction in Multizone Buildings;IEEE Transactions on Industrial Informatics;2024-01

5. Energy-Efficient GPU-Intensive Workload Scheduling for Data Centers;2023 International Conference on Machine Learning and Applications (ICMLA);2023-12-15

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