Abstract
According to the current analysis of residents’ electricity consumption behavior, with the popularization of smart meters, to a certain extent, residents’ electricity consumption data can be collected more efficiently and accurately to ensure the accuracy of subsequent electricity consumption behavior analysis. Based on the traditional fuzzy C-means clustering, clustering analysis can be performed on residential electricity consumption behavior. However, due to the large volume of data, more noise points will be generated in traditional clustering analysis, which will affect the clustering results. When studying the electricity consumption behavior of residents, based on a large amount of electricity consumption data, traditional clustering analysis will generate more noise points, which will affect the clustering results. In the study of electricity consumption behavior, the artificial neural network is introduced in the data preprocessing to classify the data. It can be found that the fuzzy C-means clustering combined with the neural network can effectively eliminate the noise points and have a good clustering effect.
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Funding from the key research and development project of Shandong province (2012CX30302) is gratefully acknowledged.
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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Mao, A., Qiao, J., Zhang, Y. (2021). Research on Residential Power Consumption Behavior Based on Typical Load Pattern. In: Fu, W., Xu, Y., Wang, SH., Zhang, Y. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-030-82562-1_46
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DOI: https://doi.org/10.1007/978-3-030-82562-1_46
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