Improving Building Energy Efficiency through Data Analysis

Author:

Phillip DiAndra1ORCID,Chen Jin2ORCID,Maksakuli Fani2ORCID,Ruci Arber2ORCID,Sturdivant E'edresha2ORCID,Zhu Zhigang3ORCID

Affiliation:

1. City College of New York, United States of America

2. Nearabl Inc., USA

3. City College of New York, USA

Publisher

ACM

Reference18 articles.

1. Scikit-Learn Developers. 2023. 4.2. permutation feature importance. https://scikit-learn.org/stable/modules/permutation_importance.html Scikit-Learn Developers. 2023. 4.2. permutation feature importance. https://scikit-learn.org/stable/modules/permutation_importance.html

2. A new methodology for building energy performance benchmarking: An approach based on intelligent clustering algorithm

3. Jane Margolies. 2022. New York developers rush to reduce emissions as hefty fines loom. https://www.nytimes.com/2022/08/16/business/new-york-real-estate-climate-change.html Jane Margolies. 2022. New York developers rush to reduce emissions as hefty fines loom. https://www.nytimes.com/2022/08/16/business/new-york-real-estate-climate-change.html

4. A prediction model based on neural networks for the energy consumption of a bioclimatic building

5. Nariman Mostafavi , Mehdi Pourpeikari Heris , Fernanda Gándara , and Simi Hoque . 2021. The Relationship between Urban Density and Building Energy Consumption. Buildings 11, 10 ( 2021 ). https://doi.org/10.3390/buildings11100455 10.3390/buildings11100455 Nariman Mostafavi, Mehdi Pourpeikari Heris, Fernanda Gándara, and Simi Hoque. 2021. The Relationship between Urban Density and Building Energy Consumption. Buildings 11, 10 (2021). https://doi.org/10.3390/buildings11100455

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