Identifying Typical Load Curves of Industrial Customers Based on DBSCAN-DPC Dual-Layer Algorithm

With the rapid development of electricity market reforms and smart grid technologies, accurately identifying the typical load curves of industrial customers is crucial for electricity retail companies in optimizing demand response strategies and enhancing market segmentation. A density-based spatial...

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Bibliographic Details
Published in2025 IEEE International Conference on Power and Integrated Energy Systems (ICPIES) pp. 434 - 439
Main Authors Ma, Yuanqian, Xu, Huan, Liang, Xueyan, Wu, Hangzhe, Wang, Yunchu, Xu, Nuo
Format Conference Proceeding
LanguageEnglish
Published IEEE 07.04.2025
Subjects
Clustering algorithms
customer clustering
Customer profiles
DBSCAN-DPC algorithm
Demand response
Electricity
Heuristic algorithms
Indexes
load characteristics
Machine learning algorithms
market segmentation
Reliability
Scalability
Smart grids
typical load curve
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DOI10.1109/ICPIES65420.2025.11070196

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Summary:With the rapid development of electricity market reforms and smart grid technologies, accurately identifying the typical load curves of industrial customers is crucial for electricity retail companies in optimizing demand response strategies and enhancing market segmentation. A density-based spatial clustering of applications with noise and density peak clustering (DBSCAN-DPC) dual-layer clustering algorithm is proposed in this paper. First, abnormal load curves are detected based on DBSCAN. Then, a multi-dimensional index is extracted to create customer profiles, and DPC is applied for secondary clustering to identify typical load curves and refine segmentation. Finally, case studies on industrial and commercial customers in eastern China demonstrate that the proposed method achieves effective market segmentation of electricity customers.
DOI:10.1109/ICPIES65420.2025.11070196