State-of-the-art on research and applications of machine learning in the building life cycle-Reference-Cited by-同舟云学术

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State-of-the-art on research and applications of machine learning in the building life cycle

Published:2020-04 Issue: Volume:212 Page:109831
ISSN:0378-7788
Container-title:Energy and Buildings
language:en
Short-container-title:Energy and Buildings

Author:

Hong Tianzhen,Wang Zhe,Luo Xuan,Zhang Wanni

Funder

U.S. Department of Energy

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Building and Construction,Civil and Structural Engineering

Reference157 articles.

1. The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants;KLEPEIS;J. Expo. Sci. Environ. Epidemiol.,2001

2. Forecasting Energy use in buildings using artificial neural networks: a review;Runge;Energies,2019

3. Modeling and forecasting building energy consumption: a review of data-driven techniques;Bourdeau;Sustain. Cities Soc.,2019

4. Leveraging machine learning and big data for smart buildings: a comprehensive survey;Qolomany;IEEE Access,2019

5. Machine learning for smart building applications: review and taxonomy;Djenouri;ACM Comput. Surv.,2019

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