Probabilistic machine learning for enhanced chiller sequencing: A risk-based control strategy-Reference-Cited by-同舟云学术

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Probabilistic machine learning for enhanced chiller sequencing: A risk-based control strategy

Published:2024-03 Issue: Volume: Page:
ISSN:2666-1233
Container-title:Energy and Built Environment
language:en
Short-container-title:Energy and Built Environment

Author:

Chen Zhe^ORCID,Zhang Jing,Xiao Fu^ORCID,Madsen Henrik^ORCID,Xu Kan

Publisher

Elsevier BV

Reference43 articles.

1. Generative pre-trained transformers (GPT)-based automated data mining for building energy management: advantages, limitations and the future;Zhang;Energy Built Environ.,2024

2. Interpretable machine learning for building energy management: a state-of-the-art review;Chen;Adv. Appl. Energy,2023

3. Hong Kong's Climate Action Plan 2050,2021

4. Physics-informed neural networks for building thermal modeling and demand response control;Chen;Build. Environ.,2023

5. A reinforcement learning-enabled iterative learning control strategy of air-conditioning systems for building energy saving by shortening the morning start period;Dai;Appl. Energy,2023

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Novel load allocation analysis for energy management and carbon emissions reduction of chiller system in hotel buildings;Journal of Building Engineering;2024-11

2. Semantic model-based large-scale deployment of AI-driven building management applications;Automation in Construction;2024-09

3. Intrinsically interpretable machine learning-based building energy load prediction method with high accuracy and strong interpretability;Energy and Built Environment;2024-08

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