Toward the application of a machine learning framework for building life cycle energy assessment

Author:

Venkatraj V.ORCID,Dixit M.K.ORCID,Yan W.,Caffey S.ORCID,Sideris P.ORCID,Aryal A.ORCID

Funder

National Science Foundation

Publisher

Elsevier BV

Subject

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

Reference103 articles.

1. U.S. Energy Information Agency. Annual Energy Outlook 2020 with projections to 2050. Annual Energy Outlook 2019 with Projections to 2050 2019;44.

2. Embodied carbon analysis and benchmarking emissions of high and ultra-high strength concrete using machine learning algorithms;Thilakarathna;J. Clean. Prod.,2020

3. Lewis M. States Act to Reduce Embodied Carbon in Public Procurement 2021.

4. COP26 Goals - UN Climate Change Conference (COP26) at the SEC – Glasgow 2021 n.d. https://ukcop26.org/cop26-goals/ (accessed December 8, 2021).

5. Data-driven approach for evaluating the energy efficiency in multifamily residential buildings;Seyrfar;Pract. Period. Struct. Des. Constr.,2021

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