Gaussian Mixture Model for Device State Classification
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Solution Overview
Problem
Current energy management systems face challenges in automatically and efficiently classifying the operational states of power-consuming devices due to the complexity of current consumption patterns and the need for manual intervention, which becomes impractical with a large number of devices, especially when real-time alerts and scalable solutions are required.
Innovation Solution
A system that uses historical data to train a Gaussian Mixture Model (GMM) to determine distinct operational states of power-consuming devices, allowing for automatic classification of current measurements into operational modes, with a monitoring device comprising a training module and a classification module to provide real-time state determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring and classification of device operational states is performed, then accuracy of state determination can be maintained, but the system becomes non-scalable and impossible to handle when a large number of circuits or devices are involved
Solution Approach 1:
The system employs machine learning models that automatically learn and classify device operational states from current consumption data without requiring manual intervention. The model trains itself on historical data and autonomously determines device states, enabling the system to scale to large numbers of devices while maintaining high accuracy through automated pattern recognition
Solution Approach 2:
The patent replaces manual mechanical analysis with electronic and computational systems. Current consumption data is captured electronically and processed through machine learning algorithms that automatically classify operational states, substituting human analysts with automated computational models that can handle large-scale device monitoring
2Productivity
If real-time automated classification is implemented, then scalability and productivity are improved, but the complexity of the system increases due to the need for machine learning models and historical data processing
Solution Approach 1:
The system performs preliminary actions by capturing and storing historical current consumption data before real-time classification is needed. This pre-captured data is used to train machine learning models in advance, so that when real-time classification is required, the models are already prepared and can immediately process new data without complex real-time training computations
Solution Approach 2:
The patent introduces machine learning models as intermediary components between raw current consumption data and operational state classification. These models serve as mediators that learn patterns from historical data and then apply learned knowledge to classify device states, simplifying the overall system architecture by separating data collection, model training, and real-time classification into distinct modular components
3Ease of operation
If traditional threshold-based classification is used, then the system remains simple to operate, but it requires manual setting of thresholds per circuit and does not adapt to changing device characteristics over time
Solution Approach 1:
The system transitions from static threshold values to dynamic, adaptive classification. Machine learning models continuously learn from historical current consumption data and automatically adjust their classification boundaries based on observed patterns. This allows the system to adapt to changing device characteristics, seasonal variations, and new operational modes without manual intervention, while maintaining ease of operation through automated model updates
4Measurement precision
If extensive historical data is collected for training, then the accuracy of operational state classification is improved, but the time and computational resources required for training increase
Solution Approach 1:
The system employs a progressive training approach where the machine learning model is trained on partial historical data sets in stages rather than requiring all data to be processed at once. This allows the model to achieve functional accuracy with subsets of data, enabling incremental improvement of classification performance while reducing the time and computational resources required for each training iteration
Data Source
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AI summary
A system for determination of a current consumer operational state operates on two sets of data respective of the current consumer. The first set of data is historical data of current consumption measured periodically. A training module of the system determines a plurality of distinct operational states of the current consumer based on the historical data. The training includes the selection of a model and then determines state parameters based on the model. Once sufficient training takes place, the system uses its classification module to classify, based on the extracted state parameters, a newly received current measurement or measurements, to a distinct operational mode of the current consumer from the plurality of distinct operational states. The training phase may be repeated periodically adding newer data to historical data, and furthermore, dropping older data as newer data is made available, and updates the states and the associated parameters.