Smart City Gnosys

Smart city article details

Title	Machine Learning-Based Prediction Of Building Water Consumption For Improving Building Water Efficiency
ID_Doc	36058
Authors	Wang L.; El-Gohary N.M.
Year	2019
Published	Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
DOI	http://dx.doi.org/10.1061/9780784482445.018
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. © 2019 American Society of Civil Engineers.
Author Keywords

Similar Articles

Id	Similarity	Authors	Title	Published
13024	0.898	Ardabili S.; Mosavi A.; Várkonyi-Kóczy A.R.	Building Energy Information: Demand And Consumption Prediction With Machine Learning Models For Sustainable And Smart Cities	Lecture Notes in Networks and Systems, 101 (2020)
23280	0.889	Huang J.; Algahtani M.; Kaewunruen S.	Energy Forecasting In A Public Building: A Benchmarking Analysis On Long Short-Term Memory (Lstm), Support Vector Regression (Svr), And Extreme Gradient Boosting (Xgboost) Networks	Applied Sciences (Switzerland), 12, 19 (2022)
57903	0.88	Daniel H.; Mantha B.R.K.; Soto B.G.D.	Towards A Review Of Building Energy Forecast Models	Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019 (2019)
22062	0.876	Mo Y.; Zhao D.; Syal M.	Effective Features To Predict Residential Energy Consumption Using Machine Learning	Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019 (2019)
54322	0.871	Ardabili S.; Abdolalizadeh L.; Mako C.; Torok B.; Mosavi A.	Systematic Review Of Deep Learning And Machine Learning For Building Energy	Frontiers in Energy Research, 10 (2022)
10905	0.867	Bui M.T.; Yáñez-Godoy H.; Elachachi S.M.	Assessment Of The Implications And Challenges Of Using Artificial Intelligence For Urban Water Networks In The Context Of Climate Change When Building Future Resilient And Smart Infrastructures	Journal of Pipeline Systems Engineering and Practice, 16, 1 (2025)
8877	0.865	Hemlata; Rai M.	An Optimized Demand For Cost And Environment Benefits Towards Smart Residentials Using Iot And Machine Learning	Sustainable Smart Homes and Buildings with Internet of Things (2024)
54287	0.864	Zhakiyev N.; Omirgaliyev R.; Bapiyev I.; Baisakalova N.; Tankeyev S.	System For Water And Electricity Consumption Prediction In Smart Cities Using Ml	SIST 2023 - 2023 IEEE International Conference on Smart Information Systems and Technologies, Proceedings (2023)
39987	0.861	Herrera, M; Izquierdo, J; Pérez-García, R; Ayala-Cabrera, D	On-Line Learning Of Predictive Kernel Models For Urban Water Demand In A Smart City	12TH INTERNATIONAL CONFERENCE ON COMPUTING AND CONTROL FOR THE WATER INDUSTRY, CCWI2013, 70 (2014)
51061	0.859	Arabasy M.; Hussein M.F.; Abu Osba R.; Al Dweik S.	Smart Housing: Integrating Machine Learning In Sustainable Urban Planning, Interior Design, And Development	Asian Journal of Civil Engineering, 26, 1 (2025)