Performance Assessment for Short-Term Water Demand Forecasting Models on Distinctive Water Uses in Korea

Kang-Min Koo, Kuk-Heon Han, Kyung-Soo Jun, Gyumin Lee, Jung-Sik Kim and Kyung-Taek Yum
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Kang-Min Koo: Graduate School of Water Resources, Sungkyunkwan University, Suwon 16419, Korea
Kuk-Heon Han: Smart Water Grid Research Group, Sungkyunkwan University, Suwon 16419, Korea
Kyung-Soo Jun: Graduate School of Water Resources, Sungkyunkwan University, Suwon 16419, Korea
Gyumin Lee: Construction and Environmental Research Center, Sungkyunkwan University, Suwon 16419, Korea
Jung-Sik Kim: Techwin Co., Ltd., Cheongju 28580, Korea
Kyung-Taek Yum: Smart Water Grid Research Group, Sungkyunkwan University, Suwon 16419, Korea

Sustainability, 2021, vol. 13, issue 11, 1-18

Abstract: It is crucial to forecast the water demand accurately for supplying water efficiently and stably in a water supply system. In particular, accurately forecasting short-term water demand helps in saving energy and reducing operating costs. With the introduction of the Smart Water Grid (SWG) in a water supply system, the amount of water consumption is obtained in real-time through a smart meter, which can be used for forecasting the short-term water demand. The models widely used for water demand forecasting include Autoregressive Integrated Moving Average, Radial Basis Function-Artificial Neural Network, Quantitative Multi-Model Predictor Plus, and Long Short-Term Memory. However, there is a lack of research on assessing the performance of models and forecasting the short-term water demand in the SWG demonstration plant. Therefore, in this study, the short-term water demand was forecasted for each model using the data collected from a smart meter, and the performance of each model was assessed. The Smart Water Grid Research Group installed a smart meter in block 112 located in YeongJong Island, Incheon, and the actual data used for operating the SWG demonstration plant were adopted. The performance of the model was assessed by using the Residual, Root Mean Square Error, Normalized Root Mean Square Error, Nash–Sutcliffe Efficiency, and Pearson Correlation Coefficient as indices. As a result of water demand forecasting, it is difficult to forecast water demand only by time and water consumption. Therefore, as the short-term water demand forecasting models using only time and the amount of water consumption have limitations in reflecting the characteristics of consumers, a water supply system can be managed more precisely if other factors (weather, customer behavior, etc.) influencing the water demand are applied.

Keywords: smart water grid; advanced metering infrastructure; short-term water demand forecasting; distinctive uses (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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