Paper
13 May 2024 User electricity load classification portrait based on multidimensional feature analysis
Dongsheng Liu, Kun Dong, Jianfeng Zhao
Author Affiliations +
Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 1315979 (2024) https://doi.org/10.1117/12.3024556
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China
Abstract
Against the backdrop of the widespread application of advanced metering systems and power Internet of Things technology, the rapid growth of electricity big data and the integration of diverse heterogeneous characteristics from various sources present unprecedented complexity. A precise understanding of user electricity consumption behavior and load characteristics holds significant importance for achieving energy efficiency improvements and personalized services. This paper aims to construct user electricity load classification profiles through multidimensional feature analysis using a non-intrusive load disaggregation method. It utilizes daily average power consumption (P), daily average operating duration (T), and daily average activation count (0) as key feature dimensions to establish the PTO model. By comprehensive assessment, the PTO model reveals characteristics such as energy consumption, operating duration, and frequency of user electricity loads. Experimental validation is conducted using the publicly available REDD dataset.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Dongsheng Liu, Kun Dong, and Jianfeng Zhao "User electricity load classification portrait based on multidimensional feature analysis", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 1315979 (13 May 2024); https://doi.org/10.1117/12.3024556
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