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http://dx.doi.org/10.3741/JKWRA.2021.54.S-1.1083

Comparative analysis of linear model and deep learning algorithm for water usage prediction  

Kim, Jongsung (Institute of Water Resources System, Inha University)
Kim, DongHyun (Department of Civil Engineering, Inha University)
Wang, Wonjoon (Department of Civil Engineering, Inha University)
Lee, Haneul (Department of Civil Engineering, Inha University)
Lee, Myungjin (Institute of Water Resources System, Inha University)
Kim, Hung Soo (Department of Civil Engineering, Inha University)
Publication Information
Journal of Korea Water Resources Association / v.54, no.spc1, 2021 , pp. 1083-1093 More about this Journal
Abstract
It is an essential to predict water usage for establishing an optimal supply operation plan and reducing power consumption. However, the water usage by consumer has a non-linear characteristics due to various factors such as user type, usage pattern, and weather condition. Therefore, in order to predict the water consumption, we proposed the methodology linking various techniques that can consider non-linear characteristics of water use and we called it as KWD framework. Say, K-means (K) cluster analysis was performed to classify similar patterns according to usage of each individual consumer; then Wavelet (W) transform was applied to derive main periodic pattern of the usage by removing noise components; also, Deep (D) learning algorithm was used for trying to do learning of non-linear characteristics of water usage. The performance of a proposed framework or model was analyzed by comparing with the ARMA model, which is a linear time series model. As a result, the proposed model showed the correlation of 92% and ARMA model showed about 39%. Therefore, we had known that the performance of the proposed model was better than a linear time series model and KWD framework could be used for other nonlinear time series which has similar pattern with water usage. Therefore, if the KWD framework is used, it will be possible to accurately predict water usage and establish an optimal supply plan every the various event.
Keywords
Water usage; Nonlinear feature; K-means; Wavelet; Deep learning;
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Times Cited By KSCI : 2  (Citation Analysis)
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