Anomalous Utility Usage Detection via Data Decomposition
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Solution Overview
Problem
Existing utility usage monitoring systems fail to detect unusual or unauthorized usage in real-time, making it difficult for homeowners and businesses to identify potential theft or anomalies in utility consumption.
Innovation Solution
A system that analyzes utility usage data by decomposing historical data into significant components using techniques like principal component analysis, flagging data that exceeds a prescribed threshold as anomalous, indicating potential unauthorized usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional utility monitoring methods are used, then device complexity is low, but measurement precision and anomaly detection capability are insufficient
Solution Approach 1:
The system performs preliminary decomposition of historical utility usage data into significant components before comparison. By pre-processing the data to extract meaningful patterns and thresholds, the system enables more precise anomaly detection without requiring complex real-time analysis infrastructure.
Solution Approach 2:
The patent introduces an intermediary processing layer that decomposes utility usage data into significant components. This intermediary step transforms raw data into a form that is easier to compare and analyze, improving detection precision while keeping the overall system architecture manageable.
2Measurement precision
If detailed utility usage data is monitored, then measurement precision improves, but loss of time in analyzing data increases
Solution Approach 1:
The system extracts only the significant components from the complete utility usage dataset. By taking out and focusing on the most relevant patterns and deviations, the system achieves precise anomaly detection while reducing the time required to analyze the full dataset.
Solution Approach 2:
Instead of analyzing all utility usage data in detail, the system applies partial action by focusing only on the significant components that deviate from normal patterns. This selective approach maintains high detection precision while minimizing analysis time.
3Productivity
If real-time anomaly detection is implemented, then productivity in identifying theft increases, but device complexity increases
Solution Approach 1:
The system performs preliminary decomposition and threshold establishment using historical data before real-time monitoring begins. This pre-processing enables rapid real-time comparison without requiring complex computational resources during the actual detection phase, thus improving productivity while controlling complexity.
Data Source
AI summary
Detecting for anomalous utility usage, including: determining with respect to the subject set of utility usage data a portion that is not associated with a predetermined set of significant components; determining that the portion that is not associated with the predetermined set of significant components exceeds a prescribed threshold; and concluding, based at least in part on the determination that the portion that is not associated with the predetermined set of significant components exceeds the prescribed threshold, that the subject set of utility usage data is anomalous.