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Electricity Theft Detection in Power Grids with Deep Learning and Random Forests
المؤلفون المشاركون
Wang, Jinkuan
Zhao, Qiang
Li, Shuan
Yao, Xu
Yingchen, Song
Han, Yinghua
المصدر
Journal of Electrical and Computer Engineering
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-10-03
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
As one of the major factors of the nontechnical losses (NTLs) in distribution networks, the electricity theft causes significant harm to power grids, which influences power supply quality and reduces operating profits.
In order to help utility companies solve the problems of inefficient electricity inspection and irregular power consumption, a novel hybrid convolutional neural network-random forest (CNN-RF) model for automatic electricity theft detection is presented in this paper.
In this model, a convolutional neural network (CNN) firstly is designed to learn the features between different hours of the day and different days from massive and varying smart meter data by the operations of convolution and downsampling.
In addition, a dropout layer is added to retard the risk of overfitting, and the backpropagation algorithm is applied to update network parameters in the training phase.
And then, the random forest (RF) is trained based on the obtained features to detect whether the consumer steals electricity.
To build the RF in the hybrid model, the grid search algorithm is adopted to determine optimal parameters.
Finally, experiments are conducted based on real energy consumption data, and the results show that the proposed detection model outperforms other methods in terms of accuracy and efficiency.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Li, Shuan& Han, Yinghua& Yao, Xu& Yingchen, Song& Wang, Jinkuan& Zhao, Qiang. 2019. Electricity Theft Detection in Power Grids with Deep Learning and Random Forests. Journal of Electrical and Computer Engineering،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1173723
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Li, Shuan…[et al.]. Electricity Theft Detection in Power Grids with Deep Learning and Random Forests. Journal of Electrical and Computer Engineering No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1173723
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Li, Shuan& Han, Yinghua& Yao, Xu& Yingchen, Song& Wang, Jinkuan& Zhao, Qiang. Electricity Theft Detection in Power Grids with Deep Learning and Random Forests. Journal of Electrical and Computer Engineering. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1173723
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1173723
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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