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Water Meter Data Management System Analysis

Paper Type: Free Essay Subject: Computer Science
Wordcount: 1674 words Published: 9th Apr 2018

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The conventional billing system for water usage involves person visits each residential and read the meter data manually. The collected data are used for billing purpose. Manual readings can cause error and can lead to corruption. Thus the billing system can become inaccurate and inefficient. There are chances of leaks and theft which could not be identified. A traditional water meters provide only total consumption of water and provides no information about when the water was consumed at each meter site. Traditional water meters requires back end billing which may not provide accurate billing.


Water Meter Data Management provides several benefits to both utilities and customer. It involves long-term meter data management for vast quantity of data received from smart meters. The data is then validated according rule engine and stored in database for billing purpose.

Water Meter Data Management (WMDM) involves smart meter data collection, planning and management. It fetches and records water meter reading periodically to identify amount of water is being used by the consumer. It also creates awareness among consumers about the consumption of water. Water meter readings are collected automatically without human intervention.

After manufacture, meters will have a universally unique ID (UUID) which will be printed on meter and will act as part of the meter’s serial number. Under normal operating conditions the Data Concentrator Unit will query a meter periodically to read its meter data. It is Data Concentrator Unit which always initiates the communication between meters.

Meter commands will be sent over radio frequency to various meters from DCU and responses is sent by meters. DCU periodically communicates with meters and collects data from them and sends to Head End Server (HES) typically through HTTP.

A WMDM system performs accurate data storage and fast management of vast quantities of data delivered by smart metering systems. This data primarily consists of usage and events that are imported from the Head End Servers and that manage the data collection in Automatic meter reading (AMR) systems. A WMDM system will mainly import the data, then validate, cleanse errors and estimates it and makes it available for analysis and billing purpose.

Each meter is integrated with SIM, WMDM make use of Existing Global System for Mobile communications (GSM) networks for sending request and receiving data. It promises fast and accurate billing. System offers alerts on leaks and suspected theft.


Project contains the following modules:

  • Head End System
  • Data Collection
  • Validation and Error Estimation
  • Visualization

Head End System

HES is used receive stream of meter data from DCU through the Advanced Meter Infrastructure (AMI). Data Concentrator Unit (DCU) communicates with several numbers of meters and collects the data from them and transmits to HES.

The data is sent in multiple frame formats and frames are of constant size of 20 bytes. The frame consists 4 bytes of header, 2 bytes of data size, 1 byte of frame id, 2 bytes of flags, 4 bytes of source address, 4 bytes of destination address, 1 byte of checksum, last 2 bytes of CRC.

HES periodically collect data from DCU and store it in different file formats such as CSV, XML, and TXT.

HES pings DCU to check whether water meter is responding or not responding. This is one of main advantage in WMDM where it alerts in case if meter is not working but water is being consumed.

READ command is used to get particular meter readings among several number of meters using meter serial number.

It is DCU which always initiates the communication between sets of meters.


Data collection allows data to be stored easily and efficiently. It easy-to-use data acquisition solution for collecting water usage information and for display and reporting purposes. It mainly concentrates on acquiring various sets of data from different file formats stored in database. Rule engines are developed to convert raw data into respective formats, processed and stored on to database.

Radio Frequency


Raw data

Converted to

Native formats

Exact data


Rule-based algorithms are developed to validate meter readings stored in database. It provides either actual data or the best possible estimate. Invalid data can be analysed to further identify the root causes of any problem.

Multiple rules can be executed simultaneously and can be prioritized to match business needs. Estimation based on consumption profiles or historical data are automatically calculated as a substitute for missing data.


This module mainly concentrates on meter data interpretation fetched from database and visualized by hourly, daily, monthly data using graphs. Visualization module is also used to Compare meter data of different customers.

Visualization is more user-friendly and also creates awareness by comparing meter data of different customer.




  • DCU communicates with meters to collect and store meter readings according to interval of 30 minutes or hourly.
  • Provides a capability to remotely access meters readings to support customer billing, service and system operation.
  • Provide processing at the meter or within system necessary for customer service or system operation application.
  • Allows customer to view meter data using graphs.

Utility Data Processing:

  • Entry, update and monitoring of data on installation and replacement of meters.
  • Data stored according to regular intervals are validated in accordance with billing standards and updated to database.
  • Validated data must be integrated to support customer billing and other system functions.

AMI Network System:

  • It provides a capability to manage vast meter data collection schedules, and alerts in case of meters problem and all other system maintenance and operations.



  • Water meter Data Management System is available 24/7.
  • Customer can view their water usage anytime.


  • The reliability of the overall application depends on the reliability of the meter data being collected.


  • In case of a failure, the meter data can be requested from DCU.
  • Vast amount data can be easily stored and updated.


  • New features can be added and system can be upgraded to meet business requirements.


  • Response times – application loading, screen open, refresh times, etc are highly responsive.
  • Processing times –Calculations of bill, importing and exporting data are done in less amount of time.
  • Query and Reporting times – The application initial loads and subsequent loads are done fast.

Fraud Tolerance

  • System identifies the tampering in meters automatically.


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