Outage Risk Prediction System Using Data Correlation
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Solution Overview
Problem
Current customer notification systems for utility outages are reactive and lack precise prediction of outage risks, leading to significant economic and societal losses, as they fail to provide timely and accurate warnings to customers and utility operators, thereby limiting the effectiveness of mitigation strategies.
Innovation Solution
A system that utilizes a correlator to combine uncorrelated outage, weather, and graph data, a machine learning system to generate risk prediction data, and a customer notification system to provide proactive alerts based on outage state of risk (SoR) assessments, leveraging Geographic Information Systems (GIS) and machine learning algorithms to create risk maps and optimize mitigation strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional reactive customer notification systems are used, then system complexity is low, but measurement precision of outage risk prediction deteriorates
Solution Approach 1:
The system segments the outage risk prediction process into distinct functional modules: a correlator system that processes uncorrelated data sources (outage history, weather, graph data), a machine learning system that generates predictions, and a customer notification system that delivers alerts. This segmentation allows each component to specialize in specific tasks, improving overall prediction precision while managing complexity through modular architecture.
Solution Approach 2:
The system performs preliminary actions by proactively predicting outages before they occur. The correlator and machine learning system analyze historical and real-time data to generate outage risk predictions in advance, enabling the notification system to alert customers beforehand. This shifts from reactive notification to proactive prediction, significantly improving measurement precision of outage risk.
2Loss of information
If traditional reactive notification systems are used, then loss of time for customer preparation is minimized, but loss of information about outage risks worsens
Solution Approach 1:
The system incorporates feedback loops where the correlator continuously receives new data (outage history, weather updates, graph data) and feeds it to the machine learning system, which updates predictions and triggers notifications. This feedback mechanism ensures that customers receive timely outage risk information while the system learns from new data, reducing information loss without requiring excessive customer preparation time.
Solution Approach 2:
By performing preliminary outage risk assessment and sending notifications before outages occur, the system provides customers with advance information about potential outages. This allows customers to prepare appropriately (activate backup systems, adjust schedules) without the system needing to wait for actual outages to occur, thereby reducing both information loss and unnecessary customer preparation time.
3Reliability
If proactive outage prediction with multiple data sources is implemented, then reliability of customer notification improves, but device complexity increases
Solution Approach 1:
The system divides the complex data processing task into segmented functional units: the correlator system handles data collection and preliminary processing from multiple sources (outage history, weather, graph data), the machine learning system performs prediction analysis, and the notification system delivers alerts. This segmentation improves notification reliability by ensuring each component performs its specialized function well, while managing overall system complexity through clear separation of concerns.
Solution Approach 2:
The correlator system merges multiple previously uncorrelated data sources (outage history, weather data, graph data) into a unified analysis framework. By combining these diverse data streams and processing them together through the machine learning system, the patent achieves more reliable outage predictions than any single data source could provide alone, while the merged structure simplifies the overall architecture compared to maintaining separate independent systems.
Data Source
AI summary
A system for customer notification system based on outage state of risk prediction, comprising a correlator system operating on a processor and configured to receive uncorrelated outage data, weather data and graph data and to generate geographically and temporally correlated outage data, weather data and graph data. A machine learning system operating on a processor and configured to receive geographically and temporally correlated outage data, weather data and graph data and forecast data and to generate prediction data. A state of risk system operating on a processor and configured to receive the prediction data and to generate state of risk data. A customer notification system operating on a processor and configured to receive the state of risk data and to generate customer notifications as a function of the state of risk.