Meter Data Correction for Photovoltaic Module AMR Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional automatic meter reading (AMR) systems face issues with data non-reading intervals due to communication errors between metering devices and servers, leading to instability in electricity data management and real-time response challenges.
Innovation Solution
A method and apparatus that monitor errors and non-reading intervals in meter data, re-request data collection, and correct errors using estimated approximate values based on historical data, ensuring valid and enhanced meter data management through an energy data correction enhancement system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional AMR systems transmit meter data through communication infrastructure, then data collection is automated, but communication errors cause data non-reading intervals and instability
Solution Approach 1:
The system performs preliminary actions by accumulating historical meter data in advance and using it to estimate and correct missing data values. When data non-reading intervals occur, the system already has historical data ready to fill the gaps, ensuring continuous and stable data management without interruption.
Solution Approach 2:
The system implements feedback mechanisms by monitoring communication status and data validity, then using this information to trigger correction processes. The system feeds back historical data to correct missing or erroneous data points, maintaining data reliability and enabling real-time response to communication issues.
2Loss of information
If the system requests retransmission of meter data during non-reading intervals, then data completeness improves, but response time increases
Solution Approach 1:
The system performs preliminary action by pre-accumulating historical meter data and using it to immediately estimate and correct missing data values. This eliminates the need for time-consuming retransmission requests, allowing the system to fill data gaps instantly using historical patterns while maintaining both completeness and rapid response.
Solution Approach 2:
The system creates a copy of historical data patterns and uses these copies to estimate and fill missing data values. By copying and applying historical data patterns rather than waiting for retransmission, the system achieves both data completeness and fast response time simultaneously.
3Reliability
If the system corrects meter data using historical data estimation, then data validity is enhanced, but measurement precision may be compromised
Solution Approach 1:
The system uses feedback mechanisms to verify and validate corrected data against historical patterns and communication status information. By continuously monitoring and comparing corrected values with historical data trends, the system ensures both validity enhancement and maintains measurement precision through iterative verification.
Solution Approach 2:
The system changes parameters by dynamically adjusting correction methods based on communication status and data validity conditions. When data non-reading intervals are detected, the system switches to historical data estimation mode, and when data is available, it uses direct transmission validation, optimizing both validity and precision through adaptive parameter changes.
Data Source
AI summary
An apparatus and a method of correcting meter data for enhancement of electricity data management in a remote automatic measuring environment of a photovoltaic module are disclosed. According to an exemplary embodiment, a meter reads energy usage of a consumer and transmits meter data on the energy usage to an automatic meter reading (AMR) server. A meter data management system monitors an error in the transmitted meter data and a non-reading interval and re-requests collection of an error in meter data and a non-reading interval to the AMR server. When an error and a non-reading interval are found even after meter data is collected on the re-requesting, the error in the meter data and the non-reading interval are corrected using an estimated approximate value based on a history of previous meter data accumulated in a database of the AMR server.