Central Prediction System for Time Series Comparison and Analysis of Water Usage Data
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8946582/ |
| _version_ | 1818917901990625280 |
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| author | Mingeun Ji Gangman Yi Jaehee Jung |
| author_facet | Mingeun Ji Gangman Yi Jaehee Jung |
| author_sort | Mingeun Ji |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T00:41:26Z |
| format | Article |
| id | doaj.art-fcce9d64a5424c92ba8992240d6eb079 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T00:41:26Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-fcce9d64a5424c92ba8992240d6eb0792022-12-21T19:59:33ZengIEEEIEEE Access2169-35362020-01-018103421035110.1109/ACCESS.2019.29633738946582Central Prediction System for Time Series Comparison and Analysis of Water Usage DataMingeun Ji0Gangman Yi1https://orcid.org/0000-0003-4664-5325Jaehee Jung2https://orcid.org/0000-0002-0932-3039Department of Multimedia Engineering, Dongguk University, Seoul, South KoreaDepartment of Multimedia Engineering, Dongguk University, Seoul, South KoreaDepartment of Information and Communication Engineering, Myongji University, Yongin, South KoreaRevenue 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.https://ieeexplore.ieee.org/document/8946582/Non-revenue watertime seriesARIMAadditive regression modelwater leakage alert system |
| spellingShingle | Mingeun Ji Gangman Yi Jaehee Jung Central Prediction System for Time Series Comparison and Analysis of Water Usage Data IEEE Access Non-revenue water time series ARIMA additive regression model water leakage alert system |
| title | Central Prediction System for Time Series Comparison and Analysis of Water Usage Data |
| title_full | Central Prediction System for Time Series Comparison and Analysis of Water Usage Data |
| title_fullStr | Central Prediction System for Time Series Comparison and Analysis of Water Usage Data |
| title_full_unstemmed | Central Prediction System for Time Series Comparison and Analysis of Water Usage Data |
| title_short | Central Prediction System for Time Series Comparison and Analysis of Water Usage Data |
| title_sort | central prediction system for time series comparison and analysis of water usage data |
| topic | Non-revenue water time series ARIMA additive regression model water leakage alert system |
| url | https://ieeexplore.ieee.org/document/8946582/ |
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