Research Article
Research on Residential Power Consumption Behavior Based on Typical Load Pattern
@INPROCEEDINGS{10.1007/978-3-030-82562-1_46, author={Anmeng Mao and Jia Qiao and Yong Zhang}, title={Research on Residential Power Consumption Behavior Based on Typical Load Pattern}, proceedings={Multimedia Technology and Enhanced Learning. Third EAI International Conference, ICMTEL 2021, Virtual Event, April 8--9, 2021, Proceedings, Part I}, proceedings_a={ICMTEL}, year={2021}, month={7}, keywords={Data mining BP neural network Fuzzy C-means clustering}, doi={10.1007/978-3-030-82562-1_46} }- Anmeng Mao
Jia Qiao
Yong Zhang
Year: 2021
Research on Residential Power Consumption Behavior Based on Typical Load Pattern
ICMTEL
Springer
DOI: 10.1007/978-3-030-82562-1_46
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.
