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Non-intrusive load disaggregation based on digital signal processing for microcontroller application
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AI
DOI：10.1109/TLA.2025.11194772.png)
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Abstract
En 中文
This paper presents a low complexity non-intrusive load monitoring (NILM) approach for residential electric power based on digital signal processing. The aim is to identify the real operating frames of each household appliance from the aggregated current signal frames. In the methodology, two detection methods are derived. The first method, named direct method, identifies the active frames of each device by identifying the most probable combination between devices in the aggregated signal. The second method, termed indirect method, identifies the active frames of a particular device by means of a projection of the aggregated signal onto a Fourier subspace representing the characteristic footprint of the device. The methodology is tested on 4 datasets collected in Argentina. and high performance metrics are achieved. A pilot test is carried out with an ATSAMD21G18 microcontroller on the Itsy Bitsy M0 Express.
Keywords:
Home appliances
Object recognition
Vectors
Training
Signal processing algorithms
Silicon
Microcontrollers
Machine learning algorithms
Indexes
Time-domain analysis
Energy Disaggregation
low complexity NILM methodogy
Signal Processing
Household Appliances
Journal
IF:
1.3
Papers:
164
Citations:
1.8K