Research on abnormal power consumption detection based on artificial intelligence

Lei Zhou; Yongjie Tong; Xiangshuang Meng

doi:10.1117/12.2682511

26 May 2023 Research on abnormal power consumption detection based on artificial intelligence

Lei Zhou, Yongjie Tong, Xiangshuang Meng

Proceedings Volume 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023); 1270017 (2023) https://doi.org/10.1117/12.2682511
Event: International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 2023, Nanchang, China

Abstract

With the proposal of dual-carbon economy, smart grid is developing towards energy conservation and emission reduction, and the abnormal power consumption of users causes serious loss of power resources. Aiming at the problems of low accuracy and slow operation efficiency of traditional abnormal power consumption detection methods, a new method combining Light GBM model with improved short-term and short-term memory network model is proposed for abnormal power consumption detection. Anomaly detection is carried out by combining sampling and Lightgbm model, and the abnormal power consumption category is given by improving the short-term and short-term memory network model. The advantages of the proposed method are analyzed through experiments. The results show that compared with traditional detection methods, this method can detect abnormal users quickly and effectively, and the detection accuracy is 97.83%. At the same time, the abnormal data is effectively classified, and the comprehensive classification accuracy is 96.61%. It provides a certain reference for the development of anomaly detection technology.

Citation Download Citation

Lei Zhou, Yongjie Tong, and Xiangshuang Meng "Research on abnormal power consumption detection based on artificial intelligence", Proc. SPIE 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 1270017 (26 May 2023); https://doi.org/10.1117/12.2682511

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available