Appliance Status Estimation via Total Power Waveform Analysis
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Solution Overview
Problem
Conventional individual electrical-appliance operation-status estimation devices require prior information about the types and number of appliances, labor-intensive updates, and high costs, making them difficult for non-professionals to apply effectively in household settings.
Innovation Solution
A dynamic algorithm using statistical inverse estimation and clustering analysis to identify appliance types and monitor operation statuses in real time based on total electrical power usage without sensor attachment, employing a k-means algorithm and Markov switching model for real-time estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a sensor is attached to each electrical appliance to monitor operation status, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts the monitoring function from individual appliance sensors and consolidates it into a single total power usage measuring device. Instead of attaching sensors to each appliance, the system measures the aggregate power consumption at the circuit level and uses signal processing to identify individual appliance operation patterns from the combined waveform data.
Solution Approach 2:
The patent creates a universal monitoring system that can track multiple different types of electrical appliances simultaneously using a single measuring device. The system processes total power usage data to identify and monitor operation statuses of various appliances without requiring appliance-specific sensors or installation for each device.
2Measurement precision
If prior information about appliance types and numbers is collected in advance, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent implements a self-service system where the monitoring device automatically identifies appliance types and updates its database without requiring user input. The system analyzes waveform patterns from total power usage data to autonomously detect when new appliances are added or removed from the circuit, eliminating the need for manual registration or configuration by the user.
Solution Approach 2:
The patent performs preliminary automated detection and classification of appliances during the initial operation period. The system continuously analyzes power consumption patterns and pre-identifies appliance types before formal monitoring begins, so that when monitoring starts, the database is already populated with accurate appliance information without requiring user setup.
3Measurement precision
If manual updates of appliance information are performed, then measurement precision is maintained, but loss of time increases
Solution Approach 1:
The patent implements a feedback mechanism where the monitoring system continuously compares expected power consumption patterns with actual measurements. When changes are detected in the total power usage waveform that indicate new appliances or changes in existing appliances, the system automatically updates its database in real-time, eliminating the need for periodic manual updates and ensuring continuous accuracy without user intervention.
Data Source
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AI summary
An individual electrical-appliance operation-status estimation device includes a total electric power usage measurement means (5), a time variation calculation means (6a), an electrical-appliance type identification means (6b), a corresponding likelihood estimation means (6c), and an individual electrical-appliance operation-status estimation means (6d). The time variation calculation means calculates time variation (jump electric power) of electric power usage. The electrical-appliance type identification means judges, from the jump electric power, about whether a new electrical appliance is added to an existing type list or an extra type is deleted from the type list. The corresponding likelihood estimation means estimates likelihood about the occurrences of events of what electrical appliance up and down jumps correspond to. The individual electrical-appliance operation-status estimation means estimates changes of operating states of the individual electrical appliances when the likelihood is inputted, thereby updating estimation of the present operating states and optimizing each estimation of the types, rated power consumption and the operating states based on predicted values and measured data of the total electric power usage by using the updated operating states and estimation result of the rated power consumption to dynamically estimate operation probabilities of the individual electrical appliances.