Non-Technical Losses Reduction by Improving the Inspections Accuracy in a Power Utility

被引：62

作者：

Ignacio Guerrero, Juan ^{[1
]}

Monedero, Inigo ^{[1
]}

Biscarri, Felix ^{[1
]}

Biscarri, Jesus ^{[1
]}

Millan, Rocio ^{[2
]}

Leon, Carlos ^{[1
]}

机构：

[1] Univ Seville, Dept Elect Technol, Seville 41011, Spain

[2] Endesa, Dept Automated Metering Management & Field Works, Seville 41004, Spain

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2018年 / 33卷 / 02期

关键词：

Data mining; decision tree; neural network; non-technical losses; power utility; text mining; NETWORKS;

D O I：

10.1109/TPWRS.2017.2721435

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The Endesa Company is the main power utility in Spain. One of the main concerns of power distribution companies is energy loss, both technical and non-technical. A non-technical loss (NTL) in power utilities is defined as any consumed energy or service that is not billed by some type of anomaly. The NTL reduction in Endesa is based on the detection and inspection of the customers that have null consumption during a certain period. The problem with this methodology is the low rate of success of these inspections. This paper presents a framework and methodology, developed as two coordinated modules, that improves this type of inspection. The first module is based on a customer filtering based on text mining and a complementary artificial neural network. The second module, developed from a data mining process, contains a Classification & Regression tree and a Self-Organizing Map neural network. With these modules, the success of the inspections is multiplied by 3. The proposed framework was developed as part of a collaboration project with Endesa.

引用

页码：1209 / 1218

页数：10

共 31 条

[1] CARDWATCH: A neural network based database mining system for credit card fraud detection
Aleskerov, E
Freisleben, B
Rao, B
[J]. PROCEEDINGS OF THE IEEE/IAFE 1997 COMPUTATIONAL INTELLIGENCE FOR FINANCIAL ENGINEERING (CIFER), 1997, : 220 - 226
[2] [Anonymous], P INT C WIR COMM SIG
[3] [Anonymous], 2012, IBM SPSS MOD 15 ALG, P386
[4] [Anonymous], 2013, IBM SPSS Modeler v. 16
[5] Bridle J. S., 1990, Neurocomputing, Algorithms, Architectures and Applications. Proceedings of the NATO Advanced Research Workshop, P227
[6] Neural fraud detection in credit card operations
Dorronsoro, JR
Ginel, F
Sanchez, C
Cruz, CS
[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS, 1997, 8 (04): : 827 - 834
[7] Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems
dos Angelos, Eduardo Werley S.
Saavedra, Osvaldo R.
Carmona Cortes, Omar A.
de Souza, Andre Nunes
[J]. IEEE TRANSACTIONS ON POWER DELIVERY, 2011, 26 (04) : 2436 - 2442
[8] Ghosh S., 1994, Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences. Vol.III: Information Systems: Decision Support and Knowledge-Based Systems (Cat. No.94TH0607-2), P621, DOI 10.1109/HICSS.1994.323314
[9] Guerrero J. I., 2016, P 5 INT C INT SYST A, P83
[10] An evolutionary strategy based on partial imitation for solving optimization problems
Javarone, Marco Alberto
[J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2016, 463 : 262 - 269

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