Automated VEE Rules Engine for Energy Consumption Anomaly Detection
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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 less accurate, especially when handling a large number of data streams.
Innovation Solution
An automated VEE rules configuration engine dynamically optimizes validation, estimation, and editing techniques for individual energy consumption data streams, employing detection and estimation rules tailored to specific anomaly durations and stream types, thereby improving data accuracy and reducing manual labor requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated VEE rules configuration engine is implemented, then data accuracy and productivity are improved, but device complexity increases
Solution Approach 1:
The system employs self-service through automated rule configuration where the VEE engine automatically generates, validates, and optimizes editing rules without requiring manual expert intervention. The system serves itself by using historical data and anomaly detection algorithms to autonomously configure validation and estimation parameters, thereby improving data accuracy while managing complexity through automation.
Solution Approach 2:
The system applies parameter changes by dynamically adjusting VEE rule parameters based on data characteristics and anomaly patterns. The configuration engine modifies validation thresholds, estimation methods, and editing parameters automatically, allowing the system to adapt to different data streams and improve measurement precision without fixed complex configurations.
2Productivity
If manual VEE techniques are used, then labor costs are high, but system complexity remains low
Solution Approach 1:
The system replaces manual mechanical processes with automated computational systems. The VEE configuration engine substitutes human analysts who manually created and validated rules with an automated system that uses algorithms, statistical methods, and machine learning to generate and optimize VEE rules, dramatically improving processing efficiency while managing complexity through software automation.
Solution Approach 2:
The system implements feedback mechanisms where the VEE engine continuously monitors data quality, validates rule effectiveness, and automatically adjusts configuration parameters based on performance metrics. This closed-loop feedback system enables the engine to self-optimize and improve productivity while maintaining manageable complexity through iterative refinement rather than complex static configurations.
3Ease of operation
If standardized VEE rules are applied to all streams, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system applies local quality by configuring VEE rules specific to each data stream's characteristics rather than using uniform standardized rules. The configuration engine analyzes individual stream properties, anomaly patterns, and data quality metrics to generate customized validation and editing rules for each stream, thereby improving measurement precision while maintaining ease of operation through automated customization.
Solution Approach 2:
The system implements dynamics by making VEE rules adaptive and configurable based on real-time data characteristics. Rather than static standardized rules, the configuration engine dynamically adjusts validation parameters, estimation methods, and editing thresholds according to each stream's specific needs, enabling both operational simplicity through automation and improved precision through customization.
Data Source
AI summary
A control system that performs VEE on consumption streams, including: data stores that provides data sets, each of the data sets comprising groups of contiguous values that correspond to correct data; a rules processor that reads the data stores and, for the each of the data sets, that creates anomalies having different durations using only the groups of contiguous values, that generates estimates for the anomalies by employing estimation techniques for each of the different durations and, for the each of the different durations, that selects a corresponding one of the estimation techniques for subsequent employment when performing VEE of subsequent energy consumption data associated with the each of the different durations for the corresponding one of the consumption streams; a peak prediction element that estimates future cumulative energy consumption and that predicts a brown out time; and a peak controller that triggers exceptional measures to manage the cumulative energy consumption in order to preclude a brown out.