Energy Consumption Prediction of Residential Buildings Using Machine Learning: A Study on Energy Benchmarking Datasets of Selected Cities Across the United States-Reference-Cited by-同舟云学术

Energy Consumption Prediction of Residential Buildings Using Machine Learning: A Study on Energy Benchmarking Datasets of Selected Cities Across the United States

Published:2021-10-28 Issue: Volume: Page:197-205
ISSN:2366-2557
Container-title:Lecture Notes in Civil Engineering
language:
Short-container-title:

Author:

Parvaneh Milad,Seyrfar Abolfazl,Movahedi Ali,Ataei Hossein,Le Nguyen Khuong,Derrible Sybil

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-7160-9_19

Reference24 articles.

1. The U.S. Energy Information Administration (EIA), https://www.eia.gov/totalenergy/data/monthly/pdf/flow/css_2019_energy.pdf, last accessed 2021/03/24.

2. Kapousouz, E., Seyrfar, A., Derrible, S., Ataei, H.: A clustering analysis of energy and water consumption in US states from 1985 to 2015. Data science applied to sustainability analysis, edited by J. Dunn and P. Balaprakash. Amsterdam, Netherlands: Elsevier (2021).

3. Trelles Trabucco, J., Lee, D., Derrible, S., Marai, G. E.: Visual Analysis of a Smart City’s Energy Consumption. Multimodal Technologies and Interaction, 3(2), 30 (2019).

4. Derrible, S.: Urban engineering for sustainability. MIT Press (2019).

5. Derrible, S., Chester, M.: Urban Infrastructure: Reflections for 2100. Independently Published (2020).

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Comparing hyperparameter tuning methods in machine learning based urban building energy modeling: A study in Chicago;Energy and Buildings;2024-08

2. Predicting the Outcome of Construction Change Disputes Using Machine-Learning Algorithms;Journal of Legal Affairs and Dispute Resolution in Engineering and Construction;2024-02