Appliance Load Monitoring Using Transfer Learning at Low Sampling Rates
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Solution Overview
Problem
Existing non-intrusive load monitoring devices face challenges such as high sampling frequency requirements, overfitting issues, and higher error rates in identifying the usage status of low-energy appliances.
Innovation Solution
An electrical appliance monitoring system utilizing transfer learning technology and ensemble learning with a processor and storage circuit to extract characteristic waveforms and on-off state identification models, reducing sampling rates and improving accuracy through a majority voting method.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If non-intrusive load monitoring devices use high sampling frequencies to extract characteristic values, then the accuracy of identifying electrical appliance usage status is improved, but the energy consumption and data processing complexity increase
Solution Approach 1:
The patent changes the parameter of sampling frequency from high to low (e.g., 1Hz or lower), and compensates for the reduced measurement precision through signal processing techniques such as wavelet transform and characteristic value extraction that work effectively at lower sampling rates
Solution Approach 2:
The patent replaces the traditional mechanical approach of high-frequency sampling with a computational approach using wavelet transform and characteristic value extraction, substituting physical measurement intensity with signal processing intelligence
2Adaptability or versatility
If non-intrusive load monitoring devices collect new data to build models for different environments, then the adaptability to different environments is improved, but the risk of overfitting increases
Solution Approach 1:
The patent performs preliminary actions by pre-training the neural network model on comprehensive training data that includes various environmental conditions and appliance types before deployment. This pre-training establishes a robust baseline that generalizes well to new environments without requiring extensive retraining
Solution Approach 2:
The patent uses transfer learning where a model trained on source domain data (one environment) is adapted to target domain data (different environment) by fine-tuning with limited new data, effectively copying knowledge from one environment to another while avoiding overfitting through the pre-established generalization capability
3Ease of manufacture
If non-intrusive load monitoring devices use low sampling rates, then the energy consumption and hardware requirements are reduced, but the error rate in identifying low-energy appliances increases
Solution Approach 1:
The patent substitutes mechanical high-frequency sampling with wavelet transform-based signal processing that can extract characteristic values from low-frequency signals, enabling accurate identification of low-energy appliances without requiring high sampling rates
Solution Approach 2:
The patent changes the approach from increasing sampling frequency to improving signal processing algorithms, using wavelet transform and characteristic value extraction that are sensitive to low-power consumption patterns even at low sampling rates
4Measurement precision
If intrusive load monitoring devices install sensors on each electrical appliance, then the accuracy of monitoring each appliance is improved, but the hardware cost and installation complexity increase
Solution Approach 1:
The patent extracts the monitoring function from individual appliance sensors and consolidates it into a single non-intrusive monitoring device that measures total power consumption at the main electrical panel, then uses signal processing to decompose and identify individual appliance usage patterns
Solution Approach 2:
The patent creates a universal monitoring device that can identify multiple different types of electrical appliances simultaneously using a single sensor setup, making the system multi-functional without requiring separate sensors for each appliance type
Data Source
AI summary
An electrical appliance monitoring system and an electrical appliance monitoring method are provided. The electrical appliance monitoring system comprises a processor and a storage circuit. The processor is electrically connected to the storage circuit. The storage circuit stores a power consumption data model and on-off state identification models. The processor executes the power consumption data model to output power timing records according to the total power timing data. The processor extracts characteristic waveforms from the power consumption data model. The processor executes the on-off state identification models. Each on-off state identification model outputs the on-off state identification timing records according to the characteristic waveforms. The processor outputs on-off state timing data for each electrical appliance according to the on-off state identification timing records outputted by the on-off state identification models. The processor generates the on-off states corresponding to each electrical appliance according to the on-off state timing data.