Meter Data Management System Sum Check Exception Reduction
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Solution Overview
Problem
Meter data management systems (MDMSs) face challenges in accurately estimating missing meter reads, leading to sum check exceptions, which increase costs and diminish the savings from automated meter readings due to mismatches between daily and interval usage values.
Innovation Solution
The system utilizes interval read data to estimate both missing daily and interval read data, allocates unaccounted usage across missing intervals, and recalculates prior estimated daily reads, incorporating load profile data for accurate usage allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If MDMS estimates missing register reads using past average daily usage (ADU) values, then missing daily usage data can be filled, but sum check exceptions occur because the estimated values do not match actual usage from interval reads
Solution Approach 1:
The system uses interval read data as feedback to validate and adjust the estimation process. By comparing estimated daily usage against actual interval read sums, the system identifies discrepancies and applies corrections to eliminate sum check exceptions while maintaining data completeness.
Solution Approach 2:
The system changes the estimation approach from using only historical ADU values to a hybrid method that incorporates interval read data. This parameter change in the estimation methodology allows the system to maintain completeness while improving accuracy by aligning estimated values with actual measured usage.
2Measurement precision
If utilities shift from monthly to daily or hourly meter readings, then billing accuracy and dynamic pricing capability improve, but data collection complexity and risk of missing reads increase
Solution Approach 1:
The MDMS is designed to handle multiple reading frequencies (monthly, daily, hourly) and multiple data sources (register reads, interval reads) within a single unified system. This multi-functionality allows utilities to implement dynamic pricing and improved billing accuracy without requiring separate systems for different reading frequencies.
Solution Approach 2:
The system performs preliminary estimation of missing reads using available data before final validation against interval reads. This preliminary action allows the system to proactively identify and correct potential sum check exceptions before billing, reducing the impact of data collection complexities.
3Measurement precision
If utilities investigate and resolve each sum check exception, then billing accuracy improves, but operational expenses increase
Solution Approach 1:
The system applies preliminary anti-action by automatically correcting estimation methods to prevent sum check exceptions before they occur. By adjusting the estimation process to align with interval read data, the system eliminates the need for subsequent investigation and resolution of exceptions, reducing operational expenses while maintaining billing accuracy.
Data Source
AI summary
Many public utilities companies employ meter data management systems (MDMSs) to manage the millions and sometimes billions of daily and interval meter read values that they collect during a single billing period. One problem with some MDMSs estimate missing daily reads based on average daily usage and these estimates do not match daily usage values determined from interval read data, causing sum check exceptions. Such exceptions are undesirable because they can lead to billing errors and lost consumer confidence in the utility, among other things. To address this, the present inventors devised an exemplary system that uses good and estimated interval read data to more accurately estimate the missing daily reads and thereby reduce occurrences of sum check exceptions. The exemplary system also estimates missing time of use and demand data based on interval read data.