NON-INTRUSIVE RESIDENTIAL PHOTOVOLTAIC IDENTIFICATION BASED ON FEATURE ANALYSIS AND MGAT-TCN MODEL

Ni Yini; Wu Zhengtao; Li Zichen; Xia Yanghong

doi:10.19912/j.0254-0096.tynxb.2024-2387

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (5) : 683-696. DOI: 10.19912/j.0254-0096.tynxb.2024-2387

NON-INTRUSIVE RESIDENTIAL PHOTOVOLTAIC IDENTIFICATION BASED ON FEATURE ANALYSIS AND MGAT-TCN MODEL

Ni Yini¹, Wu Zhengtao², Li Zichen¹, Xia Yanghong¹

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Abstract

This paper focuses on behind-the-meter PV monitoring and proposes a non-intrusive residential PV identification method based on feature analysis and a hybrid model combining a Multi-Graph Attention Network with a Temporal Convolutional Network （MGAT-TCN）. First, the grid-connection and state-switching characteristics of household two-stage PV devices are analyzed to construct and rank a comprehensive feature set. Next, a two-stage cumulative sum （CUSUM） event detection algorithm based on a multifunctional composite sliding window, is proposed to detect PV state-switching and grid-connecting behaviors. Subsequently, the MGAT-TCN model is employed to accurately identify these behaviors using an optimized subset of features. Finally, the accuracy and real-time performance of the proposed method are validated on a self-built non-intrusive experimental platform. The results demonstrate that the proposed event detection algorithm accurately locates PV behaviors, reducing time-localization errors by at least 0.3s compared to traditional algorithms. Furthermore, the MGAT-TCN model achieves an identification accuracy of over 96%, outperforming typical neural network methods by at least 2.66%.

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photovoltaic / feature selection / graph neural networks / event detection / non-intrusive residential devices monitoring

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Ni Yini, Wu Zhengtao, Li Zichen, Xia Yanghong. NON-INTRUSIVE RESIDENTIAL PHOTOVOLTAIC IDENTIFICATION BASED ON FEATURE ANALYSIS AND MGAT-TCN MODEL[J]. Acta Energiae Solaris Sinica. 2026, 47(5): 683-696 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2387

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