Non-Intrusive Load Monitoring Based on Dimensionality Reduction and Adapted Spatial Clustering

Xu Zhang, Jun Zhou, Chunguang Lu, Lei Song, Fanyu Meng and Xianbo Wang ()
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Xu Zhang: Marketing Service Center of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 311152, China
Jun Zhou: Marketing Service Center of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 311152, China
Chunguang Lu: Marketing Service Center of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 311152, China
Lei Song: Marketing Service Center of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 311152, China
Fanyu Meng: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Xianbo Wang: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Energies, 2024, vol. 17, issue 17, 1-20

Abstract: Non-invasive load monitoring (NILM) deduces changes in energy consumption patterns and operational statuses of electrical equipment from power signals in the feed line. With the emergence of fine-grained power load distribution, the importance of utilizing this technology for implementing demand-side energy management in smart grid development has become increasingly prominent. To address the issue of low load identification accuracy stemming from complex and diverse load types, this paper introduces a NILM method based on uniform manifold approximation and projection (UMAP) reduction and enhanced density-based spatial clustering of applications with noise (DBSCAN). Firstly, this paper combines the characteristics of user load under transient and steady-state conditions and selects data with significant differences to construct a load-characteristic database. Additionally, UMAP is employed to reduce the dimensionality of high-dimensional load features and rebuild a load feature database. Subsequently, DBSCAN is utilized to categorize typical user loads, followed by a correlation analysis with the load-characteristic database to determine the types or classes of loads that involve switching actions. Finally, this paper simulates and analyzes the proposed method using the electricity consumption data of industrial users from the CER–Electricity–Data dataset. It identifies the electricity load data commonly utilized by users in a specific area of Zhejiang Province in China. The experimental results indicate that the accuracy of the proposed non-invasive load identification method reaches 95%. Compared to the wavelet transform, decision tree, and backpropagation network methods, the improvement is approximately 5%.

Keywords: non-intrusive load monitoring; load feature extraction; uniform manifold approximation; spatial clustering; electrical safety (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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