Please use this identifier to cite or link to this item:
標題: 風力發電量隨機過程與 ARCH 效果之研究
Development of Stochastic Processes and ARCH Effects for Wind Power Generation
作者: Shang-Ping Sun
關鍵字: Jump Model;Stochastic Process;Time Series.;跳躍模型;隨機程序;時間序列
引用: 朱佳仁,2006,風工程概論,新北市:科技。 //呂錫民,2010,能源科技與政策的發展,台灣大學能源研究中心,台 北市。 //杜逸龍,2009,風力發電機發電量之推估,博士論文,國立台灣大學, 台北市。 //莊月璇,2000,台灣地區風速機率分佈之研究,碩士論文,國立中央 大學,桃園縣。 //陳一成,2007,台灣風場評估及風力機可用性分析-以台中風力發電 廠為例,碩士論文,國立中興大學,台中市。 //陳美源,2002,統計學,台北市:三民。 //陳卿翊,2008,台灣地區風速特性與風力發電量之統計推估,碩士論 文,南台科技大學,台南市。 //楊正光,2013,世界風力發電發展現況及未來展望,世界智慧能源週 展覽曁技術研討會,日本。 //魏武雄,2012,時間序列分析:單變量與多變量方法(二版) ,台北 市:智勝。 //謝南瑞,1992,若干機率論與分析學的關連與互動,數學傳播第 16 卷第 4 期。//Campbell, J. Y. 1997. The econometrics of financial markets, Princeton University press. //Cartea, A. & Figueroa, M. G. 2005. Pricing in electricity markets: a mean reverting jump diffusion model with seasonality. Applied Mathematical Finance, 12, 313-335. //Dickey, D. A. & Fuller, W. A. 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74, 427-431. //Engle, R. F. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007. //Engle, R. F. & Bollerslev, T. 1986. Modelling the persistence of conditional variances. Econometric reviews, 5, 1-50. //Fama, E. F. 1965. The behavior of stock-market prices. Journal of business, 34-105. //Gemmill, G. 1986. The forecasting performance of stock options on the London traded options market. Journal of Business Finance & Accounting, 13, 535-546. //Gorban, A. N., Gorlov, A. M. & Silantyev, V. M. 2001. Limits of the turbine efficiency for free fluid flow. Journal of Energy Resources Technology, 123, 311-317. //Gujarati, D. N. & Handelsh, Y. B. 2011. Econometrics by example, Palgrave Macmillan Hampshire, UK. //International Energy Agency (IEA) 2013. World Energy Outlook 2013. International Energy Agency, France. //Jarque, C. M. & Bera, A. K. 1980. Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6, 255-259. //Kienitz, J. & Wetterau, D. 2012. Financial Modelling: Theory, Implementation and Practice with MATLAB Source, John Wiley & Sons.//Kou, S. G. 2002. A jump-diffusion model for option pricing. Management science, 48, 1086-1101. //Mastro, M. 2013. Financial Models. Financial Derivative and Energy Market Valuation: Theory and Implementation in Matlab®, 1-34. //Merton, R. C. 1976. Option pricing when underlying stock returns are discontinuous. Journal of financial economics, 3, 125-144. //Phillips, J., Morgan, C. & Jacquemin, J. 2005. Understanding uncertainties in energy production estimates for offshore wind farms. Garrad Hassan and Partners. //Phillips, P. C. & Perron, P. 1988. Testing for a unit root in time serie regression. Biometrika, 75, 335-346. //Saphores, J.-D., Khalaf, L. & Pelletier, D. 2002. On jumps and ARCH effects in natural resource prices: an application to Pacific Northwest stumpage prices. American Journal of Agricultural Economics, 84, 387-400. //Shiffler, R. E. 1988. Maximum Z scores and outliers. The American Statistician, 42, 79-80. //Vuong, Q. H. 1989. Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica: Journal of the Econometric Society, 307-333. //Worldwide Wind Energy Association (WWEA) 2013. 2013 Half-year report: Worldwide Wind Energy Association, Germany. //台灣電力公司。 //經濟部能源局,2009,再生能源發展條例。
Most Countries have been promoting the concept of environmental protection and sustainable renewable energy in the recent year. Nowadays, there are several green alternative technologies, including Solar Power and Geothermal Energy. Among these technologies, Wind Power is widely used and well developed. Even though there are theoretical formulas to assess Wind Power in detail, the actual power output changes from factors like routine maintenance, or irregular repair of the machine. These uncertain factors may affect the accuracy in Wind Power. In order to solve the disagreement between the actual and the predicted power, we apply the perspective of stochastic process and time series. Two mathematical processes are used: Geometric Brownian Motion and ARCH model, for the purpose of building a wind power model. We also use 'Jump model' to explain the unexpected variation of power. Afterwards, our study would use actual data test provided by Tai- Power Company, and estimate the parameters of this study. Later, we implement Likelihood Ratio test and calculate prediction error. Finally, our study will choose the most suitable model for Wind Power, and compare with the traditional method. The result based on the maximum likelihood estimation and the prediction error shows 'Jump model' is better than the 'Non-Jump model'. In summary, ARCH is better, followed by traditional estimation method, and the next is Geometric Brownian Motion.

由於世界各國近年來不斷提倡節能省碳以及永續再生的概念,發 展環保替代能源勢在必行。目前實務上有數種替代能源方式,例如: 風力、太陽能、地熱能等,其中風力發電為普遍使用,且具有發展性 的再生能源。目前關於風力發電已有相當成熟之理論與公式,而計算 上理論與實際發電量中間存在落差,實際發電量或許會受到機台本身 故障或例行性維修而變動,這些因素會影響預測發電量的準確性。 本研究為了探討實際發電量與預估發電量的不一致情況,會從隨 機程序和時間序列的觀點,使用「幾何布朗運動」和「ARCH」兩種 數學模型建立發電量預測模型,再針對發電量短時間內會有大幅波動 的現象,加上「跳躍模型」來說明。接著,根據台灣電力公司提供之 實際發電資料,進行資料檢定並且評估模型參數,接著進行概似比檢 定以及計算預測誤差,選出本研究之最適發電模型,並與傳統推估方 式做比較。 根據最大概似法和預測誤差結果,使用跳躍之幾何布朗運動與 ARCH 模型,相較於未考慮跳躍模型對於發電量具有較佳的解釋能力, 而整體而言使用 ARCH 是較佳的,其次為傳統預測方式,接下來才 是幾何布朗運動,結果皆優於未考慮跳躍之模型。
Rights: 不同意授權瀏覽/列印電子全文服務
Appears in Collections:企業管理學系所

Files in This Item:
File Description SizeFormat Existing users please Login
nchu-103-7101023030-1.pdf1.11 MBAdobe PDFThis file is only available in the university internal network    Request a copy
Show full item record

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.