Research And Implemention Of User Abnormal Power Consumption Behavior Detection Based On Deep Learning

With the rapid development of smart grids and the widespread deployment of electricity meters,the scale of user electricity consumption data has shown tremendous growth.Timely and effective identification of abnormal power consumption behavior in the power system is essential for discovering malicious users,reducing economic losses,and maintaining the stable development of the national economy.How to detect malicious power theft behaviors of users based on power big data has attracted widespread attention from academia and industry.However,traditional methods mainly rely on machine learning algorithms using artificial features,which have poor detection accuracy,and there is room for improvement in the task of stealing electricity detection.Deep learning is a frontier field of machine learning and has achieved great success in tasks such as machine translation and sequence modeling.In this paper,the user’s electricity consumption behavior is modeled sequentially,and the algorithm and system for detecting abnormal electricity consumption behavior of users based on deep learning are researched and implemented.The main work content is as follows:1.Propose a power theft detection model based on deep recurrent neural network.Traditional methods rely on manual features,and most methods regard electricity consumption series as static data,and cannot well grasp the nature of internal time series and external influence factors.In order to effectively model the sequence information and dynamic attributes of electricity consumption records,this paper uses a deep two-way cyclic neural network to extract the sequence features of electricity consumption data and embeds user categories,holidays,and other influencing factors as external features.Finally,the two parts are spliced and merged as The input characteristics of the classifier make the detection result more accurate.2.Propose a power theft detection model based on deep attention network.Aiming at the problem of insufficient representation of behavior sequences in traditional methods,this paper conducts time-series decomposition and snapshot division of large-scale electricity consumption data,and introduces a multi-head attention mechanism on the basis of recurrent neural networks to further enhance the discrimination of key features,and through fusion models and deepening Network to improve learning effects.Finally,a large number of experimental verifications and comparative tests were carried out on the smart meter data sets of Ireland and China National Grid,which proved the effectiveness of the proposed algorithm.3.Designed and implemented a user’s abnormal behavior detection system.In order to bridge the gap between power grid business personnel in the field of deep learning technology,this paper will implement the proposed algorithm to implement an abnormal power consumption detection system with functions such as data preview,algorithm configuration,and model detection results display.The actual case shows that the system can assist business personnel to participate in model training combined with domain knowledge,and provide users with more intuitive and understandable data mining results,which proves the practicability of the system to a certain extent.