A review of the-state-of-the-art in data-driven approaches for building energy prediction-Reference-Cited by-同舟云学术

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A review of the-state-of-the-art in data-driven approaches for building energy prediction

Published:2020-08 Issue: Volume:221 Page:110022
ISSN:0378-7788
Container-title:Energy and Buildings
language:en
Short-container-title:Energy and Buildings

Author:

Sun Ying,Haghighat Fariborz,Fung Benjamin C.M.

Publisher

Elsevier BV

Subject

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

Reference153 articles.

1. IEA, “Energy efficiency: buildings.” [Online]. Available: https://www.iea.org/topics/energyefficiency/buildings/. [Accessed: 11-Sep-2019].

2. Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0;Gallagher;Energy Build.,2018

3. The suitability of machine learning to minimise uncertainty in the measurement and verification of energy savings;Gallagher;Energy Build.,2018

4. Computer-aided building energy analysis techniques;Al-Homoud;Build. Environ.,2001

5. Contrasting the capabilities of building energy performance simulation programs;Crawley;Build. Environ.,2008

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