@Article{ee.2024.041441,
AUTHOR = {Liang Zhu, Junyang Liu, Chen Hu, Yanli Zhi, Yupeng Liu},
TITLE = {Analysis of Electricity Consumption Pattern Clustering and Electricity Consumption Behavior},
JOURNAL = {Energy Engineering},
VOLUME = {121},
YEAR = {2024},
NUMBER = {9},
PAGES = {2639--2653},
URL = {http://www.techscience.com/energy/v121n9/57656},
ISSN = {1546-0118},
ABSTRACT = {Studying user electricity consumption behavior is crucial for understanding their power usage patterns. However, the traditional clustering methods fail to identify emerging types of electricity consumption behavior. To address this issue, this paper introduces a statistical analysis of clusters and evaluates the set of indicators for power usage patterns. The fuzzy C-means clustering algorithm is then used to analyze 6 months of electricity consumption data in 2017 from energy storage equipment, agricultural drainage irrigation, port shore power, and electric vehicles. Finally, the proposed method is validated through experiments, where the Davies-Bouldin index and profile coefficient are calculated and compared. Experiments showed that the optimal number of clusters is 4. This study demonstrates the potential of using a fuzzy C-means clustering algorithm in identifying emerging types of electricity consumption behavior, which can help power system operators and policymakers to make informed decisions and improve energy efficiency.},
DOI = {10.32604/ee.2024.041441}
}