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題名 利用隨機模型訂定電力之最佳契約容量
Determining the Optimal Contract Capacity of Electric Power Based on Stochastic Modeling作者 游振利 貢獻者 洪英超
游振利關鍵詞 具飄移項之布朗運動
Ljung-Box檢定
Kolmogorov-Smirnov檢定
電力契約容量最佳化日期 2015 上傳時間 17-Aug-2015 14:07:10 (UTC+8) 摘要 由於商業、工業和民生各方面大量的用電需求，使得電費在某些季節會特別昂貴。又因為電力的生產和儲存都有限，故電力公司為了能更有效率的分配總電力，要求消費者事先訂定各自用戶的契約容量，做為每個月分配電力的最大標準。對於消費者而言，相較於較高的契約容量，選取較低的契約容量通常負擔的基本電費也較低，但是當用電量超過契約容量時則必須支付高額罰金。因此消費者為了盡可能使長期的用電消費降低，選擇一個合適且最佳的契約容量是很重要的課題。在本文中以隨機模型”具飄移項之布朗運動”作為分析用電量趨勢的模型，並介紹如何做模型的驗證以及參數的估計，接著建構出總電費的期望值估計式以尋找最佳的契約容量。最後，以政治大學的實際用電量資料作為本文的研究實例，並提出選擇契約容量之建議方針。 參考文獻 [1] T.W. Anderson and D.A. Darling (2014). Asymptotic Theory of Certain "Goodness of Fit" Criteria Based on Stochastic Processes. The Annals of Mathematical Statistics, Vol.23, No.2, pp.193-212[2] R. Baldick, S. Kolos and S. Tompaidis (2014). Interruptible Electricity Contracts from an Electricity Retailer`s Point of View: Valuation and Optimal Interruption. Operations Research, Vol.54, No.4, pp.627-642[3] C.W. Cheng, Y.C. Hung and N. Balakrishnan (2014). Generating Beta Random Numbers and Dirichlet Random Vectors in R: The Package rBeta2009. Computational Statistics and Data Analysis, 71, pp.1011-1020[4] A. Dahl (2010). A Rigorous Introduction to Brownian Motion. Department of Statistics, The University of Chicago.[5] L. Decreusefond and A.S. Ustunel (1999). Stochastic Analysis of the Fractional Brownian Motion. Potential Analysis, Vol.10, Issue 2, pp.177-214[6] J. Felsenstein (1973). Maximum Likelihood Estimation of Evolutionary Trees from Continuous Characters. American Journal of Human Genetics, Vol.25, pp.471-492[7] L. Harmon, J. Weir, C. Brock, R. Glor, W. Challenger, G. Hunt, R. FitzJohn, M. Pennell, G. Slater, J. Brown, J. Uyeda and J. Eastman (2014). Package ”geiger”.URL http://cran.r-project.org/web/packages/geiger/geiger.pdf[8] S. Heydari and A. Siddiqui (2010). Real Options Analysis of Multiple-Exercise Interruptible Load Contracts. Department of Statistical Science, University College London, London, UK.[9] D.E. Knuth (1971). Optimum binary search trees. Acta Informatica, Vol.1, Issue 1, pp.14-25[10] F.J. Massey Jr. (2012). The Kolmogorov-Smirnov Test for Goodness of Fit. Journal of the American Statistical Association, Vol.46, Issue 253, pp.68-78[11] I. Negri and Y. Nishiyama (2008). Goodness of fit test for ergodic diffusion processes. Annals of the Institute of Statistical Mathematics, Vol.61, Issue 4, pp.919-928[12] S.S. Oren (2001). Integrating real and financial options in demand-side electricity contracts. Decision Support Systems archive, Vol.30, Issue 3, pp.279-288[13] L. Qi and J. Sun (1993). A nonsmooth version of Newton`s method. Mathematical Programming, Vol.58, Issue 1-3, pp.353-367[14] D.S. Stoffer and C.M.C. Toloi (1992). A note on the Ljung-Box-Pierce portmanteau statistic with missing data. Statistics & Probability Letters, Vol.13, Issue 5, pp.391-396[15] M. Subasi, N. Yildirim and B. Yildiz (2004). An improvement on Fibonacci search method in optimization theory. Applied Mathematics and Computation, Vol.147, Issue 3, pp.893-901[16] C.H. Tsaia, J. Kolibala and M. Li (2010). The golden section search algorithm for finding a good shape parameter for meshless collocation methods. Engineering Analysis with Boundary Elements, Vol.34, Issue 8, pp.738-746 描述 碩士
國立政治大學
統計研究所
102354028資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102354028 資料類型 thesis dc.contributor.advisor 洪英超 zh_TW dc.contributor.author (Authors) 游振利 zh_TW dc.creator (作者) 游振利 zh_TW dc.date (日期) 2015 en_US dc.date.accessioned 17-Aug-2015 14:07:10 (UTC+8) - dc.date.available 17-Aug-2015 14:07:10 (UTC+8) - dc.date.issued (上傳時間) 17-Aug-2015 14:07:10 (UTC+8) - dc.identifier (Other Identifiers) G0102354028 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/77550 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 統計研究所 zh_TW dc.description (描述) 102354028 zh_TW dc.description.abstract (摘要) 由於商業、工業和民生各方面大量的用電需求，使得電費在某些季節會特別昂貴。又因為電力的生產和儲存都有限，故電力公司為了能更有效率的分配總電力，要求消費者事先訂定各自用戶的契約容量，做為每個月分配電力的最大標準。對於消費者而言，相較於較高的契約容量，選取較低的契約容量通常負擔的基本電費也較低，但是當用電量超過契約容量時則必須支付高額罰金。因此消費者為了盡可能使長期的用電消費降低，選擇一個合適且最佳的契約容量是很重要的課題。在本文中以隨機模型”具飄移項之布朗運動”作為分析用電量趨勢的模型，並介紹如何做模型的驗證以及參數的估計，接著建構出總電費的期望值估計式以尋找最佳的契約容量。最後，以政治大學的實際用電量資料作為本文的研究實例，並提出選擇契約容量之建議方針。 zh_TW dc.description.tableofcontents 第一章 導論 1第二章 布朗運動之參數估計與模型檢定 3第一節 布朗運動模型(BROWNIAN MOTION) 3第二節 具飄移項的布朗運動模型(BROWNIAN MOTION WITH DRIFT) 5第三節 具飄移項布朗運動模型之參數估計 6第四節 布朗運動模型之驗證 7第三章 訂定電力最佳契約容量問題 10第一節 最佳化問題描述 10第二節 以布朗運動模型擬定最佳策略 12第四章 實例研究 19第五章 總結與討論 25參考文獻 27 zh_TW dc.format.extent 752042 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102354028 en_US dc.subject (關鍵詞) 具飄移項之布朗運動 zh_TW dc.subject (關鍵詞) Ljung-Box檢定 zh_TW dc.subject (關鍵詞) Kolmogorov-Smirnov檢定 zh_TW dc.subject (關鍵詞) 電力契約容量最佳化 zh_TW dc.title (題名) 利用隨機模型訂定電力之最佳契約容量 zh_TW dc.title (題名) Determining the Optimal Contract Capacity of Electric Power Based on Stochastic Modeling en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) [1] T.W. Anderson and D.A. Darling (2014). Asymptotic Theory of Certain "Goodness of Fit" Criteria Based on Stochastic Processes. The Annals of Mathematical Statistics, Vol.23, No.2, pp.193-212[2] R. Baldick, S. Kolos and S. Tompaidis (2014). Interruptible Electricity Contracts from an Electricity Retailer`s Point of View: Valuation and Optimal Interruption. Operations Research, Vol.54, No.4, pp.627-642[3] C.W. Cheng, Y.C. Hung and N. Balakrishnan (2014). Generating Beta Random Numbers and Dirichlet Random Vectors in R: The Package rBeta2009. Computational Statistics and Data Analysis, 71, pp.1011-1020[4] A. Dahl (2010). A Rigorous Introduction to Brownian Motion. Department of Statistics, The University of Chicago.[5] L. Decreusefond and A.S. Ustunel (1999). Stochastic Analysis of the Fractional Brownian Motion. Potential Analysis, Vol.10, Issue 2, pp.177-214[6] J. Felsenstein (1973). Maximum Likelihood Estimation of Evolutionary Trees from Continuous Characters. American Journal of Human Genetics, Vol.25, pp.471-492[7] L. Harmon, J. Weir, C. Brock, R. Glor, W. Challenger, G. Hunt, R. FitzJohn, M. Pennell, G. Slater, J. Brown, J. Uyeda and J. Eastman (2014). Package ”geiger”.URL http://cran.r-project.org/web/packages/geiger/geiger.pdf[8] S. Heydari and A. Siddiqui (2010). Real Options Analysis of Multiple-Exercise Interruptible Load Contracts. Department of Statistical Science, University College London, London, UK.[9] D.E. Knuth (1971). Optimum binary search trees. Acta Informatica, Vol.1, Issue 1, pp.14-25[10] F.J. Massey Jr. (2012). The Kolmogorov-Smirnov Test for Goodness of Fit. Journal of the American Statistical Association, Vol.46, Issue 253, pp.68-78[11] I. Negri and Y. Nishiyama (2008). Goodness of fit test for ergodic diffusion processes. Annals of the Institute of Statistical Mathematics, Vol.61, Issue 4, pp.919-928[12] S.S. Oren (2001). Integrating real and financial options in demand-side electricity contracts. Decision Support Systems archive, Vol.30, Issue 3, pp.279-288[13] L. Qi and J. Sun (1993). A nonsmooth version of Newton`s method. Mathematical Programming, Vol.58, Issue 1-3, pp.353-367[14] D.S. Stoffer and C.M.C. Toloi (1992). A note on the Ljung-Box-Pierce portmanteau statistic with missing data. Statistics & Probability Letters, Vol.13, Issue 5, pp.391-396[15] M. Subasi, N. Yildirim and B. Yildiz (2004). An improvement on Fibonacci search method in optimization theory. Applied Mathematics and Computation, Vol.147, Issue 3, pp.893-901[16] C.H. Tsaia, J. Kolibala and M. Li (2010). The golden section search algorithm for finding a good shape parameter for meshless collocation methods. Engineering Analysis with Boundary Elements, Vol.34, Issue 8, pp.738-746 zh_TW
