Machine Learning-Based Prediction of Building Water Consumption for Improving Building Water Efficiency

Lufan Wang, Nora El-Gohary

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Improving building water efficiency is crucial in meeting the long-term water needs of urban dwellers and achieving global sustainability targets. Although a body of research efforts has been conducted on modeling urban water use, there is still a limited understanding of the impacts of various factors on water consumption in different geographic regions, and of the interdependencies of building water and energy usage. Towards addressing these knowledge gaps, this paper proposes a machine learning-based model to predict the water consumption of buildings based on their physical characteristics and energy consumption levels. Building water consumption data from New York City, Boston, and Philadelphia were used. A support vector regression (SVR) algorithm was used to build the prediction model. The paper discusses the proposed model and its performance results, identifies the features that affect building water consumption and their importance patterns, and analyzes the impacts of the identified factors on water consumption in different cities.

Original languageEnglish (US)
Title of host publicationComputing in Civil Engineering 2019
Subtitle of host publicationSmart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
EditorsChao Wang, Yong K. Cho, Fernanda Leite, Amir Behzadan
PublisherAmerican Society of Civil Engineers (ASCE)
Pages139-145
Number of pages7
ISBN (Electronic)9780784482445
DOIs
StatePublished - Jan 1 2019
EventASCE International Conference on Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience, i3CE 2019 - Atlanta, United States
Duration: Jun 17 2019Jun 19 2019

Publication series

NameComputing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019

Conference

ConferenceASCE International Conference on Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience, i3CE 2019
CountryUnited States
CityAtlanta
Period6/17/196/19/19

Fingerprint

Learning systems
Water
Sustainable development
Energy utilization

ASJC Scopus subject areas

  • Computer Science(all)
  • Civil and Structural Engineering

Cite this

Wang, L., & El-Gohary, N. (2019). Machine Learning-Based Prediction of Building Water Consumption for Improving Building Water Efficiency. In C. Wang, Y. K. Cho, F. Leite, & A. Behzadan (Eds.), Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019 (pp. 139-145). (Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019). American Society of Civil Engineers (ASCE). https://doi.org/10.1061/9780784482445.018

Machine Learning-Based Prediction of Building Water Consumption for Improving Building Water Efficiency. / Wang, Lufan; El-Gohary, Nora.

Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019. ed. / Chao Wang; Yong K. Cho; Fernanda Leite; Amir Behzadan. American Society of Civil Engineers (ASCE), 2019. p. 139-145 (Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, L & El-Gohary, N 2019, Machine Learning-Based Prediction of Building Water Consumption for Improving Building Water Efficiency. in C Wang, YK Cho, F Leite & A Behzadan (eds), Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019. Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019, American Society of Civil Engineers (ASCE), pp. 139-145, ASCE International Conference on Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience, i3CE 2019, Atlanta, United States, 6/17/19. https://doi.org/10.1061/9780784482445.018
Wang L, El-Gohary N. Machine Learning-Based Prediction of Building Water Consumption for Improving Building Water Efficiency. In Wang C, Cho YK, Leite F, Behzadan A, editors, Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019. American Society of Civil Engineers (ASCE). 2019. p. 139-145. (Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019). https://doi.org/10.1061/9780784482445.018
Wang, Lufan ; El-Gohary, Nora. / Machine Learning-Based Prediction of Building Water Consumption for Improving Building Water Efficiency. Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019. editor / Chao Wang ; Yong K. Cho ; Fernanda Leite ; Amir Behzadan. American Society of Civil Engineers (ASCE), 2019. pp. 139-145 (Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019).
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