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研究生:黃國欣
研究生(外文):Guo-Sin Huang
論文名稱:用戶負載型態歸類之研究
論文名稱(外文):The Classification of Customer Load Pattern
指導教授:陸臺根陸臺根引用關係
指導教授(外文):Tai-Ken Lu
學位類別:碩士
校院名稱:國立臺灣海洋大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:113
中文關鍵詞:負載型態用戶歸類資料探勘
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電業自由化是世界的潮流,透過自由市場競爭的機制,希望能提供更良好的用電環境,提升人民生活水平。然而,電業自由化帶來的衝擊使得傳統電業解構,分為發電、輸電、配電、售電業,由於各項營運事業的分離,使得機組排程、負載預測、電力價格、電源規劃等皆面臨了新的革新。因此由用戶端來預測未來電力供需以提供消費者合適的電力商品,是未來相當重要的課題。
本文研究用戶的負載型態,做為將來電業進行電價制定、負載預測、及經營策略研擬的參考資料。但由於用戶資料多,產生的負載型態亦多,因此如何找出代表性負載型態是本文研究重點。
本文探討負載型態的步驟分為2部份,一為以自我相關函數(Autocorrelation Function,ACF)鑑定方法找出原始序列的負載型態,再透過交叉相關函數(Cross-Correlation Function,CCF)鑑定將負載型態相似的分類,最後經由歐基里德距離調整門檻值將同一分類群的負載資料加總平均得到此分類的代表性負載型態。另一為尋找用戶負載型態的屬性資料,由資料探勘工具找出特徵屬性的歸類準則。代表性負載型態的建立可做為將來機組排程、負載預測、電力價格、電源規劃等的參考資料。
Market restruction and deregulation for electric utilities are the global trend in the past years, which penetrate the open market competition mechanism and provide the good electricity service, as well as promote the living level of the people. However, the electrical industry liberalization brings the impact which deregulates the traditional electrical industry to the power generation company, the power transmission company, the power distribution company, and the selling electricity industry. As a result of the deregulation of power industry, the unit commitment, the load forecast, the spot price of electricity, the generation planning schedule and so on, have all faced the new innovation. Therefore, from the customer’s aspect, suggests the appropriate electric power products to the customer based on the forecast of the future electric power supply and demand, will be the important research issue.
This thesis studies the load pattern, which is the reference data for the electricity price formulation, the load forecast, and the management strategy planning and so on. But the user’s data are too many to the load patterns are also many, so the representative load pattern finding is the key point of this research.
The discussion of the load pattern has two parts in this thesis: one part is to use the Autocorrelation Function(ACF) analysis analysis method to find the load pattern of the original series; then use Cross-Correlation Function(CCF) analysis method to find the similar load patterns; finally, adjustment the threshold of Euclidean distance to decide the load pattern cluster, and average these load patterns as the representative load pattern of this cluster. The other part is to find the attribute materials of load pattern, and then to find the characteristic attribute material classification criterion using Data mining technology. The representative load patterns can be used as reference for the unit commitment, the load forecast, the spot price of electricity, the generation planning schedule and so on.
目錄
中文摘要.....................................................................................................i
英文摘要....................................................................................................ii
目錄............................................................................................................iii
圖目錄........................................................................................................vi
表目錄........................................................................................................xi

第一章 緒論..............................................................................................1
1-1 研究背景..........................................................................................1
1-2 研究文獻探討..................................................................................3
1-3 研究方法與目的..............................................................................8
1-4 論文內容架構..................................................................................9
第二章 電力用戶負載型態分類及歸類...................................10
2-1 電力用戶負載型態研究................................................................10
2-2 電力用戶負載型態所面臨的問題................................................11
2-3 資料歸類所需考慮的因素............................................................15
2-4 軟體工具說明................................................................................15
第三章 理論描述..................................................................................17 3-1 已知用負載型態分類...................................................................17
3-2 未知用戶負載型態歸類...............................................................32
3-2-1 探勘資料前置作業................................................................32
3-2-2 使用決策樹及關聯規則分析探勘關鍵屬性........................34
3-2-3 卡方獨立性檢定....................................................................41
第四章 模擬分析..................................................................................44
4-1 已知負載型態分類.......................................................................46
4-1-1 資料標準化............................................................................46
4-1-2 遺失值修補............................................................................48
4-1-3 用戶用電週期鑑定,用戶代表性負載型態建立................52
4-1-4 CCF鑑定,用戶代表性負載分類.......................................55
4-2 未知負載型態歸類.......................................................................64
4-2-1資料前置處理與資料轉換.....................................................63
4-2-2 關鍵性屬性資料篩選............................................................66
4-2-3 歸類結果................................................................................69
4-2-4 分類矩陣及增益圖................................................................82

第五章 結論及未來研究方向...........................................................86
5-1 結論...............................................................................................86
5-2 未來研究方向...............................................................................87
參考文獻..................................................................................................88
【附錄】各類用戶代表性負載型態................................................91
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