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Related Experiment Videos

Nonintrusive Power Load Decomposition Based on Adaptive Graph Convolutional Neural Network.

Pinzhang Zhao¹, Jian Wei¹, Lihui Wang²

¹Jiangsu Institute of Metrology, Nanjing 210023, China.

Sensors (Basel, Switzerland)

|May 27, 2026

Summary

This summary is machine-generated.

An adaptive graph convolutional neural network (AChebNet) improves nonintrusive load decomposition by learning appliance operating states. This method enhances accuracy and reduces errors in power disaggregation tasks.

Keywords:

adaptive adjacency matrix feature input dimension graph convolutional neural network nonintrusive power load decomposition

Related Experiment Videos

Area of Science:

Artificial Intelligence
Machine Learning
Signal Processing

Background:

Nonintrusive load monitoring (NILM) is crucial for understanding energy consumption.
Existing methods struggle to fully exploit correlations in appliance operating states.

Purpose of the Study:

To propose an adaptive graph convolutional neural network (AChebNet) for improved NILM.
To enhance the characterization of feature dependencies and internal connectivity in power load data.

Main Methods:

Introduced an adaptive adjacency matrix to define feature dependencies within a graph model.
Integrated an adaptive neighbor matrix into the Chebyshev Spectral CNN (ChebNet) framework.
Utilized Spearman correlation for multi-feature selection to optimize model input.

Main Results:

AChebNet achieved a 48.87% reduction in mean absolute error (MAE) for five-appliance disaggregation.
Mean power disaggregation accuracy improved from 87.39% to 92.74% compared to ChebNet.
Multi-feature inputs further reduced MAE by 16.86% and increased accuracy to 94.58%.

Conclusions:

AChebNet effectively reduces decomposition error and enhances accuracy in NILM.
The adaptive graph convolutional approach successfully captures complex feature relationships.
Multi-feature inputs significantly boost the performance of the proposed AChebNet model.