scholarly journals Unsupervised Detection of Abnormal Electricity Consumption Behavior Based on Feature Engineering

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 55483-55500 ◽  
Author(s):  
Wei Zhang ◽  
Xiaowei Dong ◽  
Huaibao Li ◽  
Jin Xu ◽  
Dan Wang
Keyword(s):  
Electricity Consumption ◽  
Feature Engineering ◽  
Consumption Behavior ◽  
Behavior Based

scholarly journals Analysis of users’ electricity consumption behavior based on ensemble clustering

2019 ◽  
Vol 2 (6) ◽  
pp. 479-488
Author(s):  
Qi Zhao ◽  
Haolin Li ◽  
Xinying Wang ◽  
Tianjiao Pu ◽  
Jiye Wang
Keyword(s):  
Electricity Consumption ◽  
Ensemble Clustering ◽  
Consumption Behavior ◽  
Behavior Based

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

2021 ◽  
Vol 11 (16) ◽  
pp. 7625
Author(s):  
Hua Li ◽  
Bo Hu ◽  
Yubo Liu ◽  
Bo Yang ◽  
Xuefang Liu ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Short Term Memory ◽  
Electricity Consumption ◽  
Classification Model ◽  
K Value ◽  
Consumption Behavior ◽  
Classification Framework ◽  
Load Fluctuation ◽  
Behavior Based ◽  
Targeted Marketing

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.

Sign in / Sign up

Export Citation Format

Share Document