Automated VEE Engine for Building Energy Data Anomaly Handling
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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 configuration engine dynamically optimizes validation, estimation, and editing techniques for individual energy consumption data streams, automatically selecting the most appropriate methods based on anomaly duration and stream type, reducing the need for manual data analyst intervention and enhancing processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated VEE techniques are implemented to process energy consumption data streams, then processing efficiency and accuracy are improved, but system complexity increases
Solution Approach 1:
The system segments the energy consumption data processing into distinct modules: data reception module, anomaly detection module, validation module, estimation module, and editing module. Each module handles specific tasks independently, improving processing efficiency while managing complexity through functional decomposition. The segmentation allows parallel processing of multiple data streams simultaneously.
Solution Approach 2:
The patent introduces intermediary components such as configuration files and parameter databases that mediate between raw data inputs and processing algorithms. These intermediaries store pre-configured validation rules, estimation parameters, and editing criteria, reducing the computational complexity of real-time processing while maintaining high accuracy through standardized intermediate representations.
2Device complexity
If manual data analyst intervention is used for VEE techniques, then system complexity is reduced, but labor costs and processing time increase
Solution Approach 1:
The system implements self-service capabilities through automated anomaly detection algorithms that independently identify, validate, estimate, and edit anomalies in energy consumption data streams without requiring manual analyst intervention. The system automatically selects appropriate processing methods based on anomaly characteristics, reducing both labor costs and processing time while maintaining consistency and accuracy across all data streams.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting processing thresholds, confidence levels, and method selection criteria based on the specific characteristics of each anomaly detected. This allows the system to automatically adapt to different data patterns and anomaly types, reducing the need for manual configuration while optimizing processing efficiency and accuracy for each specific case.
3Measurement precision
If multiple estimation techniques are employed for different anomaly durations, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system implements dynamic method selection by automatically choosing appropriate estimation techniques based on the duration and characteristics of detected anomalies. For short-duration anomalies, simpler interpolation methods are used, while longer-duration anomalies trigger more sophisticated estimation algorithms. This dynamic adaptation improves measurement precision without requiring all complex techniques to run simultaneously, thus managing processing complexity effectively.
Solution Approach 2:
The patent applies local quality by tailoring the estimation technique to the specific local characteristics of each anomaly rather than applying a uniform method across all data. Different segments of the data stream receive different processing approaches based on their unique properties, improving overall accuracy while avoiding the complexity of implementing all possible techniques for every data point.
Data Source
AI summary
A method for performing validation, estimation, and editing (VEE), including: displaying real-time electrical usage for a building on a controllable video display; executing VEE rules on each of the streams to generate and store a corresponding post VEE readings, the post VEE readings comprising tagged energy consumption data sets each associated with a corresponding one of the streams, each of the data sets comprising first groups of contiguous interval values tagged as having been validated and second groups of contiguous interval values tagged as having been edited, the first groups of contiguous interval values corresponding to correct data; for the each of the data sets, creating anomalies having different durations using only the first groups of contiguous interval values; generating estimates for the anomalies by employing estimation techniques; for each of the durations, selecting one of the estimation techniques for subsequent employment when performing VEE of subsequent energy consumption data for the corresponding one of the streams; and executing functions on the streams translated by the generating and directing directing the controllable video display to display a weather normalized usage baseline recommendation for action regarding current energy usage.