Ten questions concerning reinforcement learning for building energy management-Reference-Cited by-同舟云学术

Ten questions concerning reinforcement learning for building energy management

Published:2023-08 Issue: Volume:241 Page:110435
ISSN:0360-1323
Container-title:Building and Environment
language:en
Short-container-title:Building and Environment

Author:

Nagy Zoltan^ORCID,Henze Gregor,Dey Sourav,Arroyo Javier,Helsen Lieve,Zhang Xiangyu,Chen Bingqing,Amasyali Kadir,Kurte Kuldeep,Zamzam Ahmed,Zandi Helia,Drgoňa Ján,Quintana Matias,McCullogh Steven,Park June Young,Li Han,Hong Tianzhen,Brandi Silvio,Pinto Giuseppe,Capozzoli Alfonso,Vrabie Draguna,Bergés Mario,Nweye Kingsley,Marzullo Thibault,Bernstein Andrey

Publisher

Elsevier BV

Subject

Building and Construction,Geography, Planning and Development,Civil and Structural Engineering,Environmental Engineering

Reference167 articles.

1. Chapter 5: Increasing efficiency of building systems and technologies;of Energy (DOE);Quadrenn. Technol. Rev. Assess. Energy Technol. Res. Oppor.,2015

2. The National Human Activity Pattern Survey (NHAPS): A resource for assessing exposure to environmental pollutants;Klepeis;J. Expos. Anal. Environ. Epidemiol.,2001

3. Grid-interactive efficient buildings technical report series: Whole-building controls, sensors, modeling, and analytics;Roth,2019

4. Gnu-RL: A precocial reinforcement learning solution for building HVAC control using a differentiable MPC policy;Chen,2019

5. ASHRAE guideline 36-2018 high-performance sequences of operation for HVAC systems;ASHRAE,2018

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