计算机科学 ›› 2022, Vol. 49 ›› Issue (1): 140-145.doi: 10.11896/jsjkx.210100177
肖丁, 张玙璠, 纪厚业
XIAO Ding, ZHANG Yu-fan, JI Hou-ye
摘要: 窃电对社会和经济发展造成了重大损害。如何基于电力大数据来检测用户恶意窃电行为,已受到学术界和工业界的广泛关注。针对传统方法依赖于手工特征、行为序列表征不足和检测精度差等问题,提出了一种基于多头注意力机制的窃电检测模型(Electricity Theft Detection Based on Multi-Head Attention,ETD-MHA)。该模型基于双向门控循环神经网络(Bidirectional Gated Recurrent Unit,BiGRU)充分捕获用户用电行为序列的时序特征,引入多头注意力机制来进一步增强关键特征的区分度,并通过加深网络来提高学习效果。在爱尔兰和中国国家电网智能电表数据集上进行了大量的实验,结果表明,相比传统的逻辑回归(Linear Regression,LR)、支持向量机(Support Vector Machine,SVM)、随机森林(Random Forest,RF)等多种算法,所提模型展现出了明显的优势。例如,在爱尔兰智能电表数据集上,其AUC值相比LR算法最高提升了34.6%。
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[1]DEPURU S S S R,WANG L,DEVABHAKTUNI V.Electricity theft:Overview,issues,prevention and a smart meter based approach to control theft[J].Energy Policy,2011,39(2):1007-1015. [2]NAVANI J P,SHARMA N K,SAPRA S.Technical and Non-Technical Losses in Power System and Its Economic Consequence in Indian Economy[J].International Journal of Electro-nics & Computer Science Engineering,2012,1(2):757-761. [3]AKOUEMO H N,POVINELLI R J.Probabilistic anomaly detection in natural gas time series data[J].International Journal of Forecasting,2016,32(3):948-956. [4]NIZAR A H,DONG Z Y,WANG Y.Power Utility Nontechnical Loss Analysis With Extreme Learning Machine Method[J].IEEE Transactions on Power Systems,2008,23(3):946-955. [5]NAGI J,YAP K S,TIONG S K,et al.Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines[J].IEEE Transactions on Power Delivery,2010,25(2):1162-1171. [6]DEPURU S S S R,WANG L,DEVABHAKTUNI V,et al.A hybrid neural network model and encoding technique for enhanced classification of energy consumption data[C]//2011 IEEE Power and Energy Society General Meeting.IEEE,2011:1-8. [7]DEPURU S S S R,WANG L,DEVABHAKTUNI V.Supportvector machine based data classification for detection of electricity theft[C]//2011 IEEE/PES Power Systems Conference and Exposition.IEEE,2011:1-8. [8]NAGI J,YAP K S,TIONG S K,et al.Improving SVM-BasedNontechnical Loss Detection in Power Utility Using the Fuzzy Inference System[J].IEEE Transactions on Power Delivery,2011,26(2):1284-1285. [9]JOKAR P,ARIANPOO N,LEUNG V C.Electricity Theft Detection in AMI Using Customers' Consumption Patterns[J].IEEE Transactions on Smart Grid,2017,7(1):216-226. [10]LUONG M T,PHAM H,MANNING C D.Effective approaches to attention-based neural machine translation[J].arXiv:1508.04025,2015. [11]CHEN P,LIU S,SHI C,et al.NeuCast:Seasonal Neural Forecast of Power Grid Time Series[C]//Twenty-Seventh International Joint Conference on Artificial Intelligence IJCAI-18.International Joint Conferences on Artificial Intelligence Organization.2018:3315-3321. [12]ZHUANG S J,YU Z Y,GUO W Z,et al.Short Term Load Forecasting via Zoneout-based Multi-time Scale Recurrent Neural Network[J].Computer Science,2020,47(9):105-109. [13]HU T Y,GUO Q L,SUN H B.Nontechnical loss detectionbased on stacked uncorrelating autoencoder and support vector machine[J].Automation of Electric Power Systems,2019,43(1):119-127. [14]MENG Z,XU X.A Hybrid Short-Term Load ForecastingFramework with an Attention-Based Encoder-Decoder Network Based on Seasonal and Trend Adjustment[J].Energies,2019,12(24):4612. [15]ZHENG Z,YANG Y,NIU X,et al.Wide and Deep Convolutio-nal Neural Networks for Electricity-Theft Detection to Secure Smart Grids[J].IEEE Transactions on Industrial Informatics,2017:14(4):1606-1615. [16]NABIL M,MAHMOUD M,ISMAIL M,et al.Deep recurrentelectricity theft detection in AMI networks with evolutionary hyper-parameter tuning[C]//2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber,Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).IEEE,2019:1002-1008. [17]CHEN Z,MENG D,ZHANG Y,et al.Electricity theft detection using deep bidirectional recurrent neural network[C]//2020 22nd International Conference on Advanced Communication Technology (ICACT).IEEE,2020:401-406. [18]BAHDANAU D,CHO K,BENGIO Y.Neural Machine Translation by Jointly Learning to Align and Translate[J].arXiv:1409.0473,2014. [19]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[J].arXiv:1706.03762,2017. [20]ISSDA.Data from the commission for energy regulation[EB/OL].[2019-07-01].http://www.ucd.ie/issda/data/commissionforenergyregulationcer/. |
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