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

Kang-Min Koo,Kuk-Heon Han,Kyung-Taek Yum,Gyumin Lee,Jung-Sik Kim,Kyung-Soo Jun

doi:10.3390/su13116056

Kang-Min Koo, Kuk-Heon Han + Show 4 more

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https://doi.org/10.3390/su13116056

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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.

Highlights

The Smart Water Grid Research Group (SWGRG) operated the smart water grid (SWG) demonstration plant in block 112 located in YeongJong Island, Incheon from 2017 to2019 [1]
Several studies have been conducted in this regard, but there is a lack of research on assessing the performance of short-term water demand forecasting at the types of water use with higher temporal resolution
Where ∅1, ···, ∅ p are the coefficients of AR(p) to be estimated that accompany each of the observations in the past periods, p is the order of AR terms, θ1, ···, θq are the coefficients of moving average (MA)(q) will be stationary, q is the order of MA terms, c is constant, L is backshift operator (e.g., Lyt = yt−1 ), d is the order of non-seasonal differences, and et is the white noise, which is an error term that satisfies N 0, σ2

Summary

Introduction

The Smart Water Grid Research Group (SWGRG) operated the smart water grid (SWG) demonstration plant in block 112 located in YeongJong Island, Incheon from 2017 to2019 [1]. The Smart Water Grid Research Group (SWGRG) operated the smart water grid (SWG) demonstration plant in block 112 located in YeongJong Island, Incheon from 2017 to. The integrated system monitors and collects the data on the amount of water consumption at the types of water use through real-time remote reading using Advanced Metering Infrastructure (AMI) sensors and a bilateral network having transmission and control devices. The SWGRG installed 527 ultrasonic-wave-type AMI sensors in the customers of block 112 located in YeongJong Island and collected the water consumption data at one-hour intervals in real-time. The collected water consumption data can be used to forecast water demand and to determine abnormal water pressure or leakage in worn-out pipelines throughout the water supply infrastructure [3].

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