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Apply Graph Signal Processing on NILM: An Unsupervised Approach Featuring Power Sequences
Bochao Zhao1, Xuhao Li1, Wenpeng Luan1
1School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
Sensors (Basel, Switzerland)
|April 28, 2023
Summary
This study introduces a new unsupervised method for non-intrusive load monitoring (NILM) using state transition sequences (STS). This approach improves appliance energy disaggregation accuracy without extra sensors.
Area of Science:
- Electrical Engineering
- Computer Science
- Signal Processing
Background:
- Non-intrusive load monitoring (NILM) provides appliance-level electricity insights without additional sensors.
- Existing unsupervised graph signal processing (GSP) methods for NILM can be improved through enhanced feature selection.
Purpose of the Study:
- To propose a novel unsupervised GSP-based NILM approach (STS-UGSP) utilizing state transition sequences (STS) for improved feature extraction.
- To enhance the performance of NILM by introducing a new feature representation and matching algorithm.
Main Methods:
- Extraction of state transition sequences (STS) from power readings.
- Graph generation using dynamic time warping distances between STSs for similarity quantification.
- A forward-backward power STS matching algorithm for operational cycle analysis.
Main Results:
- The proposed STS-UGSP method was validated on three public datasets.
- STS-UGSP outperformed four benchmark methods in two evaluation metrics.
- The approach provided more accurate appliance energy consumption estimates compared to ground truth.
Conclusions:
- The novel STS-UGSP approach offers a promising advancement in unsupervised NILM.
- Feature extraction using STS enhances the accuracy of load disaggregation.
- This method provides a cost-effective solution for demand-side management.
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