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研究生: 郭千瑜
論文名稱: 智慧電網中以戶為單位之用電特徵分析
An Analysis of Household Electricity Meter Data in Smart Grid Systems
指導教授: 陳伶志
學位類別: 碩士
Master
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2013
畢業學年度: 101
語文別: 中文
論文頁數: 63
中文關鍵詞: 智慧型電表資料分析回看法支持向量回歸分群演算法
英文關鍵詞: Smart Meter Data, Data Analysis, ε-LookBack-N, Support Vector Regression, Clustering
論文種類: 學術論文
相關次數: 點閱:260下載:18
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  • 智慧電網及智慧型電表建置在全球快速發展,在台灣已有特定地區裝設智慧型電網,透過智慧型電表蒐集用戶電表量測資料。消費者的用電習慣各有不同,而影響消費者的用電習慣有許多因素,本研究將會針對溫度、樓層等因素作用電量分析,使消費者不但可以瞭解自身的用電習慣,並加以調整,以減少電費支出,還可節省電能消耗。除了電量分析外,預測用電量也可幫助電力業者適時調整發電量,改善浪費電力能源之現象。本研究使用三種用電預測方法,分別為回看法(ε-LookBack-N)、差分整合自回歸移動平均模型(Autoregressive Integrated Moving Average Model)和支持向量回歸(Support Vector Regression),我們將評估其適用性與準確度,並透過用電戶的用電特徵分群,進一步結合環境變因,研究用電戶用電度數的預測模型,並利用既有量測資料進行驗證。其預測模型可以幫助電力業者作用電預測,適時調整發電量,有效率的配送電能,以達到節能省碳之目的。

    誌謝. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i 摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii 第一章緒論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 第二章電表資料描述. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 第三章多樣性分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 第一節用電量基本分析. . . . . . . . . . . . . . . . . . . . . . . . . 6 第二節依樓層分析用電量. . . . . . . . . . . . . . . . . . . . . . . 8 第三節依溫度分析用電量. . . . . . . . . . . . . . . . . . . . . . . 11 第四節依溫度與樓層分析用電量. . . . . . . . . . . . . . . . . . 13 第五節依假日分析用電量. . . . . . . . . . . . . . . . . . . . . . . 15 第四章用電戶分群. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 第一節分群演算法. . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 第二節分群效度指標. . . . . . . . . . . . . . . . . . . . . . . . . . 20 第三節文化基因演算法. . . . . . . . . . . . . . . . . . . . . . . . . 23 第五章用電戶用電預測方法. . . . . . . . . . . . . . . . . . . . . . . . . . 26 第一節ε-LookBack-N . . . . . . . . . . . . . . . . . . . . . . . . . . 26 第二節ARIMA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 第三節SVR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 第六章預測結果. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 第七章相關文獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 第八章結論與未來展望. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 參考文獻 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 附錄A 多樣性分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 附錄B SVR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

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