Automated VEE Processor for Energy Consumption Data Streams
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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, resulting in reduced accuracy and increased costs.
Innovation Solution
An automated VEE processor and configuration engine dynamically optimize VEE techniques for each energy consumption data stream, employing detection and estimation rules tailored to anomaly durations, replacing manual data analyst efforts and enabling real-time configuration and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data analyst efforts are employed for validation, estimation, and editing of energy consumption data, then customization for individual data streams can be achieved, but labor intensity and costs increase significantly
Solution Approach 1:
The system enables automated self-service through the configuration engine that automatically generates and optimizes VEE rules without requiring manual data analyst intervention. The processor autonomously performs validation, estimation, and editing operations on energy consumption data streams, eliminating the need for human analysts while maintaining high accuracy through algorithmic rule generation and dynamic optimization.
Solution Approach 2:
The patent replaces the mechanical system of manual data analysis with an automated electronic processing system. The configuration engine and processor substitute human analysts by implementing automated rule generation, anomaly detection, and data correction algorithms that perform VEE operations electronically, thereby reducing labor intensity while preserving measurement precision.
2Productivity
If generic VEE techniques are applied to all energy consumption data streams, then processing efficiency is maintained, but accuracy is reduced due to lack of customization
Solution Approach 1:
The system implements dynamic adaptability through the configuration engine that continuously generates and optimizes VEE rules based on characteristics of individual data streams. Rather than applying static generic techniques, the system dynamically adjusts validation, estimation, and editing parameters to match specific anomaly patterns and durations in each stream, thereby achieving both high processing efficiency and customized accuracy.
Solution Approach 2:
The patent applies local quality by tailoring VEE techniques to specific segments of data streams based on their unique characteristics. The configuration engine identifies different anomaly types and durations within individual streams and applies customized rule sets locally to each segment, ensuring that each data stream receives appropriate processing attention while maintaining overall system productivity.
3Measurement precision
If VEE rules are dynamically optimized for each data stream, then accuracy is significantly enhanced, but system complexity increases
Solution Approach 1:
The system manages complexity through segmentation by dividing the VEE process into distinct functional modules: the configuration engine for rule generation, the processor for execution, and separate handling of validation, estimation, and editing operations. This modular segmentation allows dynamic optimization for each data stream while containing system complexity within manageable, independent components that can be configured and maintained separately.
Data Source
AI summary
An apparatus is provided for performing validation, estimation, and editing (VEE) on interval based energy consumption streams. The apparatus includes a VEE processor and a VEE configuration engine. The VEE processor executes VEE rules on each of the interval based energy consumption streams within a specified time period to generate and store corresponding post VEE readings that are stored in a post VEE readings data stores. The post VEE readings include tagged energy consumption data sets that are each associated with a corresponding one of the interval based energy consumption streams. Each of the tagged energy consumption data sets has groups of contiguous interval values tagged as having been validated. The VEE configuration engine reads the post VEE readings upon initiation of an event and, for the each of the tagged energy consumption data sets, creates anomalies having a plurality of different durations using only the groups of contiguous interval values, and generates estimates for the anomalies by employing estimation techniques and, for each of the different durations, selects one of the estimation techniques for subsequent employment when performing VEE of subsequent energy consumption data for the corresponding one of the p interval based energy consumption streams.