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| 008 | 200605s2018 th uu m rtt 0 a eng d | ||
| 035 | _a.b12307166 | ||
| 099 | 9 | _aAIT Thesis no.ET-18-06 | |
| 100 | 0 | _aPradya Panyainkaew | |
| 245 | 1 | 0 | _aIrregular power consumption identification by using support vector machine and neural network classification |
| 260 |
_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2018 |
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| 300 |
_a59 leaves : _bill. (some col.) + _e1 online resource |
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| 490 | 1 |
_aThesis ; _vno. ET-18-06 |
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| 500 | _aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Energy | ||
| 502 | _aThesis (M.Eng.) - Asian Institute of Technology, 2018 | ||
| 520 | _aIn this thesis, support vector machine (SVM) is proposed to identify irregular power consumption which can lead to non-technical loss (NTL) in power distribution system, NTL include faulty metering, equipment failure and electrical fraud. The classifier uses customers{u2019} historical power consumption information in 2016 to investigate suspicious instances which cause irregular power consumption behavior. SVM and ANN require training set data of power consumption for classifier development and test set data for evaluation performance. Training set data contains314 irregular power usage instances and 500 regular power consumption instances. Test set data consistsof100 irregular power consumption instances and the other500 regular ones. Moreover, these information are divided into two scenarios, 249 weekdays and 117 weekend/holidays in 2016, respectively. Every instances in both scenarios are represented by individual average power consumption over 96 fifteen-minute interval a day. To represent consumption characteristic as a probability distribution function, Gaussian mixture distribution which is a feature extraction method, is derived from average power consumption. To cluster various power consumption patterns with the same characteristic, k-means clustering method is applied to both the average power consumption over 96 intervals and Gaussian mixture distribution of combined training and test set data. Using training set of data, SVM classifier is developed by creating a linear hyperplane to separate irregular and regular power consumption instances from each other with maximum margin between both regular and irregular power consumption instances boundary. Subsequently, the classifier with a higher than 85% detection rate of each cluster is used to identify irregular power consumption instances in the same cluster of testing set data based on the area under ROC curve (AUC) and accuracy/detection rate criteria. For feature extraction comparison, SVM with Gaussian mixture distribution provides a higher AUC and accuracy than SVM with the average power consumption for both weekday and holidays. To compare with ANN, SVM with Gaussian mixture distribution render a higher accuracy of 92-95% than ANN with both Gaussian mixture distribution (88-92%) and average power consumption (87-91%) for both weekday and weekend/holidays scenarios. SVM with Gaussian mixture distribution is potentially viable to irregular power consumption identification for distribution utilities. | ||
| 650 | 0 | _aSupport vector machines | |
| 650 | 0 | _aNeural networks (Computer science) | |
| 650 | 0 | _aEnergy consumption | |
| 700 | 0 |
_aWeerakorn Ongsakul, _eChairperson |
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| 700 | 1 |
_aSingh, Jai Govind, _eExamination Committee |
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| 700 | 0 |
_aThan Lin, _eExamination Committee |
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| 700 | 0 |
_aWarodom Khamphanchai, _eExamination committee |
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| 710 | 2 |
_aPEA, _eScholarship donor |
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| 710 | 2 |
_aAsian Institute of Technology Education Cooperation Project, _eScholarship donor |
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| 710 | 2 |
_aRoyal Thai Government Fellowship, _eScholarship donor |
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| 810 | 2 |
_aAsian Institute of Technology. _tThesis ; _vno. ET-18-06 |
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| 856 |
_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B02373 |
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