Statistical Dynamic Meter Data Validation Offloading
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Solution Overview
Problem
Traditional methods for validating data from smart meters on the electrical grid require significant CPU resources and often result in delayed data presentation, as they rely on processing historical trends in real-time or batch processing, leading to inefficiencies.
Innovation Solution
Pre-calculating key historical statistical data during off-peak times and staging it for use during peak times to offload processing, combined with a flexible user-defined validation algorithm that applies mathematical and logical functions to validate meter data in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time or batch processing of historical trends is used to validate meter data, then data validation can be performed, but CPU resources are significantly consumed and data presentation is delayed
Solution Approach 1:
The patent pre-calculates historical statistical data (mean, standard deviation, minimum, maximum values) during off-peak periods and stores them for later use. This preliminary computation eliminates the need for resource-intensive real-time historical analysis during peak validation periods, thereby reducing CPU consumption while maintaining validation quality and enabling faster data presentation.
2Loss of time
If real-time processing is used to validate meter data, then data presentation timeliness is improved, but CPU footprint increases significantly
Solution Approach 1:
Statistical parameters are pre-computed and stored during off-peak periods, allowing real-time validation to use these pre-prepared values instead of recalculating historical trends. This approach maintains rapid real-time response while dramatically reducing CPU resource consumption during peak operational periods.
Solution Approach 2:
The system alternates between pre-computation phases (during off-peak periods) and validation phases (during peak periods). This periodic operation pattern allows resource-intensive calculations to be performed when system load is low, while maintaining high-speed validation capability when data arrives, thus balancing CPU usage with processing timeliness.
3Reliability
If traditional validation methods are used, then comprehensive data validation is achieved, but processing efficiency decreases
Solution Approach 1:
The patent extracts only the essential statistical parameters (mean, standard deviation, min, max) from the complete historical data set and stores them for validation use. This extraction approach maintains comprehensive validation capability by preserving the key characteristics needed for validation while eliminating the need to process entire historical data sets, thereby significantly improving processing efficiency.
Data Source
AI summary
Systems, methods, and other embodiments are disclosed for validating measured meter data. In one embodiment, a graphical user interface is provided that facilitates configuration of a validation algorithm by a user. Historical usage data is accessed during a non-peak time to offload processing from a peak time. The non-peak time corresponds to a time span when the measured meter data is not being received and/or is not being validated, and the peak time corresponds to a different time span when the measured meter data is being validated. Statistical data is generated from the historical usage data during the non-peak time and stored in a memory. The measured meter data is received and the statistical data is accessed during the peak time. The measured meter data is validated by applying the validation algorithm to the statistical data and the measured meter data during the peak time.