TY - JOUR
T1 - Central Prediction System for Time Series Comparison and Analysis of Water Usage Data
AU - Ji, Mingeun
AU - Yi, Gangman
AU - Jung, Jaehee
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - Revenue water flow is defined as the amount of water for which the water rate has been collected, against tap water production, whereas non-revenue water (NRW) is defined as water that has been produced, but for which payment cannot be charged. In South Korea, there are big differences in NRW among the regions, and the NRW ratio in urban areas is higher than in rural regions. To reduce regional differences and effectively manage the water system, a management system to lower the NRW ratio is required. In particular, the NRW ratio can be reduced through an automatic leakage detection and sensor-error automatic checking system for feed water pipes and piping in household, and through leakage detection of water supply and drainage pipes that transport large volumes of water. Therefore, this study develops a system that can generate automatic alarms whenever abnormal usage is predicted via analysis of household water flow rate. Linear regression, ARIMA model, and additive regression model are compared to find the best method with high accuracy. The proposed method can support efficient water system management to lower the NRW ratio.
AB - Revenue water flow is defined as the amount of water for which the water rate has been collected, against tap water production, whereas non-revenue water (NRW) is defined as water that has been produced, but for which payment cannot be charged. In South Korea, there are big differences in NRW among the regions, and the NRW ratio in urban areas is higher than in rural regions. To reduce regional differences and effectively manage the water system, a management system to lower the NRW ratio is required. In particular, the NRW ratio can be reduced through an automatic leakage detection and sensor-error automatic checking system for feed water pipes and piping in household, and through leakage detection of water supply and drainage pipes that transport large volumes of water. Therefore, this study develops a system that can generate automatic alarms whenever abnormal usage is predicted via analysis of household water flow rate. Linear regression, ARIMA model, and additive regression model are compared to find the best method with high accuracy. The proposed method can support efficient water system management to lower the NRW ratio.
KW - additive regression model
KW - ARIMA
KW - Non-revenue water
KW - time series
KW - water leakage alert system
UR - http://www.scopus.com/inward/record.url?scp=85078705981&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2963373
DO - 10.1109/ACCESS.2019.2963373
M3 - Article
AN - SCOPUS:85078705981
SN - 2169-3536
VL - 8
SP - 10342
EP - 10351
JO - IEEE Access
JF - IEEE Access
M1 - 8946582
ER -