Evaluating Computational Methodologies for Grading Buildings on Energy Performance Using Machine Learning Techniques-Reference-Cited by-同舟云学术

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Evaluating Computational Methodologies for Grading Buildings on Energy Performance Using Machine Learning Techniques

Published:2022-05-24 Issue: Volume: Page:
ISSN:
Container-title:Computing in Civil Engineering 2021
language:
Short-container-title:

Author:

Seyrfar Abolfazl¹²,Ataei Hossein¹²

Affiliation:

1. Ph.D. Candidate, Dept. of Civil, Materials, and Environmental Engineering, Univ. of Illinois at Chicago, Chicago, IL.

2. Clinical Associate Professor, Dept. of Civil, Materials, and Environmental Engineering, Univ. of Illinois at Chicago, Chicago, IL.

Publisher

American Society of Civil Engineers

Link

http://ascelibrary.org/doi/pdf/10.1061/9780784483893.026

Reference23 articles.

1. An integrated data-driven framework for urban energy use modeling (UEUM)

2. A novel building information modeling-based method for improving cost and energy performance of the building envelope.;AlizadehKharazi B.;Int. J. Eng,2020

3. EnergyStar++: Towards more accurate and explanatory building energy benchmarking

4. Chen T. and Guestrin C. (2016). “Xgboost: A scalable tree boosting system.” Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining San Francisco CA 785–794.

5. Chicago Data Portal. (2018). Chicago Energy Benchmarking - 2017 Data Reported in 2018. (Apr. 10 2021).

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