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Electricity consumer dishonesty is a problem faced by all power utilities. Finding efficient measurements for detecting fraudulent electricity consumption has been an active research area in recent years. This paper presents a new approach towards (NTL) analysis for electric utilities using a novel intelligence-based technique, Support Vector Machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) in Malaysia to reduce its NTLs in the distribution sector due to electricity theft. The proposed model preselects suspected customers to be inspected onsite for fraud based on irregularities and abnormal consumption behavior. This approach provides a method of data mining and involves feature extraction from historical customer consumption data. The SVM based approach uses customer load profile information to expose abnormal behavior that is known to be highly correlated with NTL activities. The result yields classification classes that are used to shortlist potential fraud suspects for onsite inspection, based on significant behavior that emerges due to irregularities in consumption. Simulation results prove the proposed method is more effective compared to the current actions taken by TNB in order to reduce NTL activities. This paper presents a framework to identify and detect NTL activities in the electric utility market i.e., customers with irregular and abnormal consumption patterns indicating fraudulent activities. An automatic feature extraction method for load profiles with a combination of Support Vector Machines (SVMs) is used to identify fraud customers. This study uses historical customer consumption data collected from TNBD. Customer consumption patterns are extracted using data mining and statistical techniques, which represent customer load profiles. Based on the assumption that load profiles contain abnormalities when a fraud event occurs, SVM classifies load profiles of customers for detection of fraud suspects. There are several different types of fraud that can occur, but our research concentrates only on scenarios where abrupt changes appear in load profiles, indicating fraudulent activities.
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Electricity consumer dishonesty is a problem faced by all power utilities. Finding efficient measurements for detecting fraudulent electricity consumption has been an active research area in recent years. This paper presents a new approach towards (NTL) analysis for electric utilities using a novel intelligence-based technique, Support Vector Machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) in Malaysia to reduce its NTLs in the distribution sector due to electricity theft. The proposed model preselects suspected customers to be inspected onsite for fraud based on irregularities and abnormal consumption behavior. This approach provides a method of data mining and involves feature extraction from historical customer consumption data. The SVM based approach uses customer load profile information to expose abnormal behavior that is known to be highly correlated with NTL activities. The result yields classification classes that are used to shortlist potential fraud suspects for onsite inspection, based on significant behavior that emerges due to irregularities in consumption. Simulation results prove the proposed method is more effective compared to the current actions taken by TNB in order to reduce NTL activities. This paper presents a framework to identify and detect NTL activities in the electric utility market i.e., customers with irregular and abnormal consumption patterns indicating fraudulent activities. An automatic feature extraction method for load profiles with a combination of Support Vector Machines (SVMs) is used to identify fraud customers. This study uses historical customer consumption data collected from TNBD. Customer consumption patterns are extracted using data mining and statistical techniques, which represent customer load profiles. Based on the assumption that load profiles contain abnormalities when a fraud event occurs, SVM classifies load profiles of customers for detection of fraud suspects. There are several different types of fraud that can occur, but our research concentrates only on scenarios where abrupt changes appear in load profiles, indicating fraudulent activities.
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Happy moments 3 a
1.
August 8,2015- Our
6th years of giving and sharing love to each other.....!!!
2.
3.
4.
5.
6.
7.
...ung feeling na
araw araw bday ko pag kasama kita n masaya tayo...
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
..all ur surprises
and gifts,.i love it.!
21.
22.
23.
24.
i love you!!!
25.
26.
27.
28.
...ang sarap gumising s umaga....na ikaw
ang kasama hanggang sa aking pagtanda....
29.
30.
31.
32.
...kung nasan k,..andun
din ako!!! MARLON JHEN
33.
....pag kinakantahan mo ko,..khit baduy
sa totoo lng kinikilig ako,..para akong hinaharana ng maganda mong boses....(Buti kp!!!)
34.
35.
,,,ngiti k din....GANITO??? ....kunti
lng......!
36.
37.
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40.
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44.
45.
46.
47.
48.
49.
I LOVE YOU... ANG BEBEH Q...PABEBE N NGAUN....
50.
51.
52.
53.
MARJEN08 MARLON JENNIFER
54.
......always happy...just to be with
you...i love you so much bebeh q!!!
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