应用DBN深度学习算法的电能计量反窃电技术研究
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引用本文:刘 岩 ,袁瑞铭,郑思达,杨晓坤,王玉君.应用DBN深度学习算法的电能计量反窃电技术研究[J].计算技术与自动化,2021,(4):151-155
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刘 岩 ,袁瑞铭,郑思达,杨晓坤,王玉君 (国网冀北营销服务中心(计量中心)北京 102208) 
中文摘要:针对窃电问题严重阻碍建立公平、合理的用户秩序的问题,基于云计算的智能电网大数据处理平台SP-PPP (smart power system big data processing platform in cloud environment,SP-DPP),提出了融合自适应加权融合算法和深度置信网络DBN(Deep Belief Networks,DBN)学习算法的反窃电系统,采用DBN逐层贪婪训练算法对大数据进行处理,并利用双层RBM结构,构建出DBN深度学习算法,对获取的电能计量窃电信息进行归一化处理,将获取的宏观高纬度数据信息转换为容易识别和计算的低纬度数据。实验表明,本研究的算法识别率高,稳定性能好。
中文关键词:窃电  SP-DPP  自适应加权融合算法  深度置信网络  逐层贪婪训练算法
 
Research on Anti-stealing Technology of Electric Energy Metering Based on DBN Deep Learning Algorithm
Abstract:Aiming at the problem of electricity theft seriously hindering the establishment of a fair and reasonable user order,An anti-theft system that combines adaptive weighted fusion algorithm and deep belief network (Deep Belief Networks, DBN) learning algorithm is proposed based on Smart power system big data processing platform in cloud environment (SP-DPP),big data is processed by using DBN layer-by-layer greedy training algorithm, and a DBN deep learning algorithm is constructed by using the double-layer RBM structure, which can normalize the acquired electricity metering information and convert the acquired macro high-latitude data information into low-latitude data that is easy to identify and calculate. Tests show that the algorithm of this study has high recognition rate and good stability.
keywords:electricity theft  SP-DPP  adaptive weighted fusion algorithm  deep confidence network  layer-by-layer greedy training algorithm
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