Automated VEE Rule Configuration for Energy Stream Anomalies
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Solution Overview
Problem
Real-time and near real-time energy consumption systems face challenges in accurately processing energy consumption data due to intermittent errors and anomalies, leading to suboptimal validation, estimation, and editing (VEE) techniques, which are often labor-intensive and lack customization for individual data streams.
Innovation Solution
An automated VEE rules configuration engine dynamically optimizes detection, estimation, and editing techniques for each energy consumption data stream, using post-VEE readings to adjust rules and select the most appropriate estimation methods based on anomaly duration, reducing the need for manual data analyst intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated VEE rules configuration is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system performs self-configuration by automatically generating and optimizing VEE rules based on analyzed energy consumption data patterns, eliminating the need for manual analyst intervention in rule creation and maintenance
Solution Approach 2:
The configuration engine pre-generates multiple candidate VEE rules and pre-analyzes data patterns before actual VEE processing occurs, allowing the system to select optimal rules in real-time without complex runtime decision-making
2Loss of time
If manual data analyst intervention is reduced, then loss of time is improved, but measurement precision may worsen
Solution Approach 1:
The system continuously monitors VEE processing results and data patterns, using this feedback to automatically refine and optimize validation rules, ensuring precision is maintained or improved over time without manual intervention
Solution Approach 2:
Manual analytical processes are replaced with automated computational algorithms that systematically analyze data patterns, apply validation logic, and generate estimates using consistent mathematical methods rather than variable human judgment
3Adaptability or versatility
If customization for individual data streams is increased, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system applies different validation and estimation rules tailored to each specific data stream's characteristics and anomaly patterns, allowing customization at the local level without requiring complex global configuration management
Solution Approach 2:
The configuration system divides the overall VEE process into independent, modular rule sets that can be individually generated, optimized, and applied to different data streams, reducing the complexity of managing customization across the entire system
Data Source
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AI summary
An apparatus is provided for configuring validation, estimation, and editing (VEE) rules for performing VEE on a plurality of interval based energy consumption streams. The apparatus includes a post VEE readings data stores and a rules processor. The post VEE readings data stores provides a plurality of tagged energy consumption data sets that are each associated with a corresponding one of the plurality of interval based energy consumption streams. Each of the plurality of tagged energy consumption data sets has first groups of contiguous interval values tagged as having been validated and second groups of contiguous interval values tagged as having been edited. The rules processor, reads the post VEE readings data stores upon initiation of an event and, for the each of the plurality of tagged energy consumption data sets, creates a plurality of anomalies having a plurality of different durations using only the first groups of contiguous interval values, and generates a plurality of estimates for the plurality of anomalies by employing a plurality of estimation techniques and, for each of the plurality of different durations, selects one of the plurality of estimation techniques for subsequent employment when performing VEE of subsequent energy consumption data for the corresponding one of the plurality of interval based energy consumption streams.