Classification of Electricity Consumption Behavior Based on Improved K-Means and LSTM

Power big data-based artificial intelligence or data mining methods, which can be used to analyze electricity consumption behavior, have been widely applied to provide targeted marketing services for electricity consumers. However, the traditional clustering algorithm has difficulty in judging new e...

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Bibliographic Details
Published in:Applied sciences Vol. 11; no. 16; p. 7625
Main Authors: Li, Hua, Hu, Bo, Liu, Yubo, Yang, Bo, Liu, Xuefang, Li, Guangdi, Wang, Zhenyu, Zhou, Bowen
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.08.2021
Subjects:
Algorithms
Behavior
Big Data
Classification
Cluster analysis
Clustering
deep neural network
Electricity
electricity consumption behavior
Forecasting
improved k-means algorithm
Load
LSTM
Methods
Neural networks
power big data
Principal components analysis
ISSN:2076-3417, 2076-3417
Online Access:Get full text
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Summary:Power big data-based artificial intelligence or data mining methods, which can be used to analyze electricity consumption behavior, have been widely applied to provide targeted marketing services for electricity consumers. However, the traditional clustering algorithm has difficulty in judging new electricity consumption patterns. Deep neural networks usually need large amounts of labeled data. However, there are few comparable electricity consumption features or basic data, and the labeled data cannot meet the actual needs. Therefore, an intelligent classification framework for electricity consumption behavior based on an improved k-means and long short-term memory (LSTM) is proposed, which not only extracts features effectively, but also establishes a mapping relationship between unlabeled electricity consumption behavior characteristics and user types. The features can be labeled to train the deep neural network to judge the electricity consumption behavior of new users. Firstly, nine typical characteristics were selected from aspects including electricity price sensitivity and load fluctuation rate. Secondly, the k value and initial clustering centers of the k-means algorithm were optimized. Thirdly, the users were labelled based on the clustering results, together with the features, and a dataset was formed, which was input into LSTM to train the classification model. Finally, the analysis of users in Shenyang, China, showed the results based on the proposed method were consistent with the actual situation. Moreover, compared to other methods, the efficiency and accuracy were higher.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app11167625