Smart water meter for automatic meter reading

Clean water is one of the main sectors in smart city that need well management. One of the clean water management is utilization of water meters. The smart meter is more suitable applied for smart city. Recent Smart Water Meter allows water authorities to obtain water consumption data remotely. It also provides ability to collect and record the data in real time that can be utilised for multipurpose. However, in Indonesia, the water meters are used only to measure the total volume of clean water consumption for billing purpose only using mechanical water meter and requires labour intensive manual. Currently, many researches on smart meter design have been developed. However, the smart meter only measure and record the water consumption, without ability in which customer can determine the amount of water as needed. This paper describes design and development of smart water metering with Internet of Things. Flow meter is used as a sensor of water flowing through the pipe. The ability of the proposed smart meter is not only to measure and to record the volume water consumed, but also the customer can determine the water desired and required. The volume of water measured by the smart meter is compared with the manual measurement. The result shows that the water measured manually differs slightly from smart meter measurement using water flow sensor. The maximum difference, error, is 0.03 litres. The proposed smart meter has ability to close the main valve once the determined amount of water is reached.

Water Demand Management and Household Level Metering in Low-Income Areas of Urban Africa

Increasing the level of water metering is an objective of most initiatives for the operational restructuring of African water utilities promoted by donors and development agencies from the 1990s. Water metering penetration is a common benchmarking indicator to measure the performances of water utilities. In contrast with other benchmarks and targets set for the African water sector, which remain largely unmet, water metering at household and at water point levels are quite successful. The study discusses the arguments behind the widespread acceptance of the target of 100% metering, focusing on the suitability of household level metering for low-income settlements of urban Africa. An empirical analysis shows that metering is not an effective water demand management tool for domestic consumption, probably due to the fact that average consumption is already low, and it can hardly be reduced further. The case study shows that universal metering ambitions might discourage household level connections.

Assessment of the Metering and Billing Systems of Water Supply in Some Parts of Osogbo Metropolis, Osun State Nigeria

This study assessed the water metering and billing methods in some parts of Osogbo metropolis of Osun State, Nigeria by evaluating the adequacy, effectiveness and cost implications of the systems. Data was obtained through a questionnaire survey, field observations and oral interviews. A total number of 200 questionnaires were randomly distributed to the households around the metered neighbourhood, 173 responses were retrieved for analysis. It was discovered that the waterworks only have a metering system for 3222 (0.85%) households. It charges a fixed price of ₦2000/month for those without water meters and ₦2700/month for metered households. The percentages of domestic, industrial, religious, governmental and institutional water users were 41.6%, 30.6%, 8.7%, 8.1% and 11.0% respectively and a majority of the water users agreed to be consuming between 75 – 100 litres of water per day. Considering the number of days that households get water, the study found that in a week, 16.8%, 46.8% and 36.4% of the respondents get water for, 1-2, 2-4, and 4-7days respectively. The study revealed that 9.8%, 23.1%, 48.6% and 18.5% of the respondents were 5-20%, 21-40%, 41- 60% and 61-80% satisfied with their billing methods respectively. If water is available all the days of the week, the study found the billing system to be fair. Residents are then advised to subscribe to the metering and billing systems for fair bargaining.

Advancing the cyberinfrastructure for smart water metering: A new open source water end use disaggregation algorithm

<p>Water end use disaggregation aims to separate household water consumption data collected from a single residential water meter into appliance/fixture-level consumption data. In recent years, the field has rapidly expanded as the value of disaggregated data has been shown for understanding water use behavior, identifying anomalies, and identifying opportunities for conserving water. Several methods have been developed for disaggregating water end uses from high temporal resolution water use data collected using residential smart water meters. However, most existing methods have been incorporated into proprietary software tools and have been tested using datasets that are inaccessible due to privacy issues, with the result being that neither the code nor the data from these studies are available for verification or potential reuse. We describe and demonstrate a new, open source, and reproducible water end use disaggregation and classification tool that builds upon the results of existing smart water metering and end use disaggregation studies. The tool was designed and developed in Python and can be accessed via any current Python programming environment. It was tested on anonymized, high temporal resolution datasets collected from 31 residential dwellings located in the Cities of Logan and Providence, Utah, USA for a period of one month. Results from different meter types and sizes were tested to demonstrate the accuracy and reproducibility of the tool in disaggregating and classifying high temporal resolution data into individual water end use events. Execution of the tool requires approximately one minute for processing one-day of data collected at a four second time interval for one dwelling. The disaggregation tool is open source and can be adapted to specific research needs. The anonymized dataset we used to develop and test the tool is openly available in the HydroShare data repository.</p>