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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:262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

    [1] R. http://www.r-project.org/.
    [2] Taiwan Power Company. http://www.taipower.com.tw/.
    [3] E. Castillo, B. Guijarro, and A. Alonso. Electricity load forecast using functional
    networks. In Electricity Load Forcast Using Intelligent Technologies,
    page 75–84. EUNITE: The European Network on Intelligent Technologies for
    Smart Adaptive Systems, 2002.
    [4] C.-C. Chang and C.-J. Lin. Libsvm: A library for support vector machines.
    ACM Trans. Intell. Syst. Technol., 2(3):27:1–27:27, May 2011.
    [5] R.-S. Chang, C.-Y. Kuo, and Y.-H. Ho. A survival analysis based approach for
    mdms missing data treatment. In Wireless and Sensor Networks Conference,
    number 61, August 2012.
    [6] B.-J. Chen, M.-W. Chang, and C.-J. Lin. Load forecasting using support
    vector machines: a study on eunite competition 2001. Power Systems, IEEE
    Transactions on, 19(4):1821–1830, 2004.
    [7] D. L. Davies and D. W. Bouldin. A cluster separation measure. Pattern
    Analysis and Machine Intelligence, IEEE Transactions on, PAMI-1(2):224–
    227, 1979.
    [8] J. C. Dunn. Well-separated clusters and optimal fuzzy partitions. Journal of
    Cybernetics, 4(1):95–104, 1974.
    [9] D. Esp. Adaptive logic networks for east slovakian electrical load forecasting.
    In Electricity Load Forcast Using Intelligent Technologies, page 55–74. EUNITE:
    The European Network on Intelligent Technologies for Smart Adaptive
    Systems, 2002.
    [10] A. Jain and B. Satish. Clustering based short term load forecasting using
    support vector machines. In PowerTech IEEE Bucharest, page 1–8, 2009.
    [11] W. Karush. Minima of functions of several variables with inequalities as side
    constraints. Master’s thesis, Dept. of Mathematics, Univ. of Chicago, 1939.
    [12] L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: An Introduction
    to Cluster Analysis (Wiley Series in Probability and Statistics). Wiley-
    Interscience, 1st. edition, March 2005.
    [13] W. Kowalczyk. Averaging and data enrichment: Two approaches to electricity
    load forecasting. In Electricity Load Forcast Using Intelligent Technologies,
    page 209–218. EUNITE: The European Network on Intelligent Technologies
    for Smart Adaptive Systems, 2002.
    [14] H. W. Kuhn and A. W. Tucker. Nonlinear programming. In Second Berkeley
    Symposium on Mathematical Statistics and Probability, page 481–492, 1951.
    [15] A. Lewandowski, F. Sandner, and P. Protzel. Prediction of electricity load
    by modeling the temperature dependencies. In Electricity Load Forcast Using
    Intelligent Technologies, page 107–114. EUNITE: The European Network on
    Intelligent Technologies for Smart Adaptive Systems, 2002.
    [16] T. W. Liao. Clustering of time series data—a survey. Pattern Recognition,
    38(11):1857 – 1874, 2005.
    [17] A. Lotfi. Application of learning fuzzy inference systems in electricity load
    forecast. In Electricity Load Forcast Using Intelligent Technologies, page 123–
    130. EUNITE: The European Network on Intelligent Technologies for Smart
    Adaptive Systems, 2002.
    [18] J. B. MacQueen. Some methods for classification and analysis of multivariate
    observations. In Proc. of the fifth Berkeley Symposium on Mathematical
    Statistics and Probability, volume 1, page 281–297. University of California
    Press, 1967.
    [19] R. De Maesschalck, D. Jouan-Rimbaud, and D.L. Massart. The mahalanobis
    distance. Chemometrics and Intelligent Laboratory Systems, 50(1):1 – 18,
    2000.
    [20] J. Mercer. Functions of positive and negative type and their connection with
    the theory of integral equations. Philos. Trans. Royal Soc. (A), 83(559):69–
    70, November 1909.
    [21] M. K. Pakhira, S. Bandyopadhyay, and U. Maulik. Validity index for crisp
    and fuzzy clusters. Pattern Recognition, 37(3):487 – 501, 2004.
    [22] E. Pelikán. Middle-term electrical load forecasting by time series decomposition.
    In Electricity Load Forcast Using Intelligent Technologies, page 167–
    176. EUNITE: The European Network on Intelligent Technologies for Smart
    Adaptive Systems, 2002.
    [23] A. P. Reynolds, G. Richards, and V. J. Rayward-Smith. The application of
    k-medoids and pam to the clustering of rules. In Intelligent Data Engineering
    and Automated Learning – IDEAL 2004, volume 3177 of Lecture Notes in
    Computer Science, page 173–178. Springer Berlin Heidelberg, 2004.
    [24] W. Shen, V. Babushkin, Z. Aung, and W. L. Woon. An ensemble model for
    day-ahead electricity demand time series forecasting. In Proceedings of the
    fourth international conference on Future energy systems, page 51–62, 2013.
    [25] A. J. Smola and B. Schölkopf. A tutorial on support vector regression. Statistics
    and Computing, 14(3):199–222, 2004.
    [26] V. N. Vapnik. Statistical learning theory. Wiley, 1st. edition, September 1998.
    [27] R. Xu and D. Wunsch. Survey of clustering algorithms. Neural Networks,
    IEEE Transactions on, 16(3):645–678, 2005.

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