TY - GEN
T1 - Machine Learning-Based Prediction of Building Water Consumption for Improving Building Water Efficiency
AU - Wang, Lufan
AU - El-Gohary, Nora M.
N1 - Funding Information:
This research is based upon work supported by the Strategic Research Initiatives (SRI) Program by the College of Engineering at the University of Illinois at Urbana-Champaign.
Publisher Copyright:
© 2019 American Society of Civil Engineers.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
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U2 - 10.1061/9780784482445.018
DO - 10.1061/9780784482445.018
M3 - Conference contribution
AN - SCOPUS:85068780783
T3 - Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
SP - 139
EP - 145
BT - Computing in Civil Engineering 2019
A2 - Cho, Yong K.
A2 - Leite, Fernanda
A2 - Behzadan, Amir
A2 - Wang, Chao
PB - American Society of Civil Engineers
T2 - ASCE International Conference on Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience, i3CE 2019
Y2 - 17 June 2019 through 19 June 2019
ER -