碩士 / 國立高雄應用科技大學 / 電機工程系博碩士班 / 96 / This thesis proposes support vector machine based pattern recognition technique to classify cultured customer electricity theft by establishing and comparing with the various rational load patterns. First, in this research, Taiwan western coach cultured customers’ historical electricity data is collected and analyzed to derive summer and non-summer rational daily load patterns. Moreover, SVM network model is applied to train the selected cultured customer data set to establish the cultured customer electricity theft classifier and then the electricity theft electricity KWH can be derived by analyzing and recognizing historical data in database.
Besides, the man machine interface of server and database design which contains logical schema and physical schema as well as the data transformation service program is developed. In this research, data transformation service technique is developed to extract, transfer, and load data from customer information system to proposed SQL server database. Finally, real testing data which covered the business districts of Changhua, Yunlin, Chiayi, and Pingtong at Taipower are selected for computer simulation to demonstrate the practicality and effectiveness of the proposed method. The derived electricity theft information can effectively support Taipower business district for electricity theft investigation.
Keywords: Cultured customer electricity theft model, Pattern recognition technique, Support vector machine, Data transformation
| Identifer | oai:union.ndltd.org:TW/096KUAS0442067 |
| Date | January 2008 |
| Creators | Chun-Hsien Li, 李俊賢 |
| Contributors | Ming-Yuan Cho, 卓明遠 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 116 |
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