SDN Bandwidth Analytics for Power Forecasting Accuracy
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Solution Overview
Problem
Electric grid operators face challenges in predicting abnormal power usage patterns, leading to inaccuracies in demand forecasting, which can result in significant mismatches between generation and load, despite advances in Advanced Metering Infrastructure and 'smart' devices that provide accurate data for future estimates.
Innovation Solution
Implementing a Software Defined Network (SDN) system that monitors bandwidth usage in real-time, correlates it with power usage behavior, and notifies control area operators to adjust power generation forecasts, using unique identifiers like IP addresses and MAC addresses to characterize user behavior and detect abnormal patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional forecasting methods using historical data and weather information are used, then forecasting can be performed, but forecasting accuracy deteriorates when predicting abnormal usage patterns
Solution Approach 1:
The patent introduces bandwidth usage data as an intermediary indicator to infer power usage behavior. Network operators collect bandwidth usage data from SDN networks, which serves as a proxy to detect abnormal usage patterns without directly measuring power consumption. This intermediary data source enables more accurate forecasting by capturing real-time behavioral changes that traditional methods miss.
Solution Approach 2:
The system continuously monitors bandwidth usage and provides feedback to adjust power forecasts dynamically. By detecting abnormal bandwidth usage patterns in real-time and comparing them against historical data, the system can update power consumption predictions continuously, improving accuracy for both normal and abnormal usage scenarios.
2Measurement precision
If real-time bandwidth monitoring is implemented, then abnormal usage patterns can be detected, but system complexity increases
Solution Approach 1:
The patent leverages the existing SDN network infrastructure for dual purposes: its primary function of data transmission and a secondary function of power usage monitoring. By using the same network elements (switches, routers) that handle data traffic to also collect bandwidth metrics for power forecasting, the system avoids adding separate dedicated monitoring hardware, thus reducing overall system complexity.
Solution Approach 2:
The system merges power usage monitoring capabilities with the existing network monitoring infrastructure. By combining the collection of network traffic data and power consumption data into a unified monitoring framework, the patent reduces redundancy and simplifies the overall system architecture while maintaining real-time detection capabilities.
3Measurement precision
If more data sources are integrated for forecasting, then forecasting accuracy improves, but data processing requirements increase
Solution Approach 1:
The patent segments the data processing into distinct analytical layers: collecting raw bandwidth data from network elements, analyzing patterns to detect abnormal usage, and generating adjusted power forecasts. This segmentation allows each component to focus on specific processing tasks, improving overall efficiency by avoiding the need to process all data through every stage.
Data Source
AI summary
A computer-implemented method, a controller, and a Software Defined Network (SDN) perform steps of correlating users based on unique identifiers to service addresses; receiving historical data from associated control area operator for the service addresses; correlating the users' bandwidth usage behavior to the users' power usage behavior; monitoring the users' bandwidth usage over time; characterizing bandwidth usage type for the users with abnormal bandwidth usage patterns; and notifying the control area operator of the users with the abnormal bandwidth usage patterns based on the characterized bandwidth usage type. Accordingly, electric power forecasting by the control area operator can include improved accuracy through correlating the users' bandwidth usage behavior and detecting abnormal conditions.