@inproceedings{d5066c203ab24bc6b98fbbf15a5924db,
title = "Estimation of water demand in residential building using machine learning approach",
abstract = "This paper shows an estimation model for residential water consumption using machine learning approach in Korea. We verify the diversifying elements constituting apartment buildings as input datasets for the Back- Propagation Neural Network (BPNN), the most novel supervised learning neural network based model in accordance with the empirical water use data. A water use prediction for residential buildings is a complex and nonlinear function of geographic, climatic, and morphological variables of buildings. For the verification purpose, empirical data sets consisting of water usage data retrieved from multiple residential apartment buildings in Korea were analyzed as case studies. The proposed model accurately forecast water uses for each examined residential apartment buildings. The results of the proposed models could offer a reliable water supply to meet the useful needs of customers and the local community while facilitating the efficient consumption of water.",
keywords = "Buildings, Estimation, Neural networks, Predictive models, Reliability, Water conservation, Water resources",
author = "Dongjun Suh and Hyunyoung Kim and Jinsul Kim",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 5th International Conference on IT Convergence and Security, ICITCS 2015 ; Conference date: 24-08-2015 Through 27-08-2015",
year = "2015",
month = oct,
day = "5",
doi = "10.1109/ICITCS.2015.7292979",
language = "English",
series = "2015 5th International Conference on IT Convergence and Security, ICITCS 2015 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2015 5th International Conference on IT Convergence and Security, ICITCS 2015 - Proceedings",
address = "United States",
}