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| Title: | 風速資料的時間序列模型分析
The time series analysis for modelling wind speed data |
| Authors: | 傅偉翔
Fu, Wei-Hsiang |
| Contributors: | 鄭宗記
Cheng, Tzong-Jih
傅偉翔
Fu, Wei-Hsiang |
| Keywords: | 風速
颱風
澎湖東吉島
單根檢定
時間序列模型
韋伯分配模型
SARIMA混合迴歸模型
Box-Cox轉換
BATS模型
TBATS模型
Wind speed
Typhoon
Penghu dongi island
Unit Root Tests
Time series model
Weibull model
SARIMA model combined with regression
Box-Cox transformation
BATS model
TBATS model |
| Date: | 2020 |
| Issue Date: | 2020-09-02 11:43:02 (UTC+8) |
| Abstract: | 因冬季有東北季風吹拂，夏季有颱風侵襲，台灣地區每個月平均風速不同，利用時間序列模型建模可以解釋風速的趨勢。以往風速的建模研究只考慮到ARIMA(p,d,q)模型，且對於某地區的風速氣候趨勢也沒有深入探討，另外，由於風速的分布是接近韋伯分配的右偏分布，將Box-Cox轉換應用在ARMA模型的研究過去的文獻也較少探討。本研究本研究以台灣澎湖東吉島地區的風速的時間序列資料為例，時間點從西元1980年到2018年，將每年的時間序列資料配適韋伯分配模型、SARIMA混合迴歸模型、BATS模型與TBATS模型。研究結果發現：比起經過Box-Cox轉換的BATS模型與TBATS模型，SARIMA混合迴歸模型的配適度與預測能力表現更佳，澎湖東吉島地區的風速氣候趨勢也並非一直都是冬季較強夏季較弱。
Due to the monsoon in winter and typhoon in summer, the monthy average of wind speed in Taiwan is differet. We employe time series models to analyze the trend of wind speed. However, most of past researches only use ARIMA model and lack of explaining the climate of the wind speed in one place. Also, using the Box-Cox transformation in ARMA model has few references. This study analyzes hourly the wind speed data in Penghu dongi island in Taiwan which starts from 1980 to 2018, and fits there time series data with Weibull model, SARIMA model combined with regression, BATS and TBATS model for each year. The outcome shows that compare with BATS and TBATS model using Box-Cox transformation, SARIMA model combined with regression is better in the goodness-of-fit and prediction accuracy. Futhermore, the wind is not always strong in wind and weak in summer every year in this island. |
| Reference: | 中文文獻
1.王時鼎(1992)。侵台颱風路徑、強度、結構及風雨整合研究，國科會防災科技研究報告，NSC80-0414-P052-02B
2.吳彥儒(2007)。從隨機共整合角度檢驗股利評價模型-以臺灣股市股利。未出版之碩士論文，高雄市，國立中山大學經濟研究所。
3.凌拯民、劉秉勳、陳卿翔(2009)。台灣地區風速機率分佈函數之建立與特性分析。論文發表於科技學刊，第18卷，科技類，第1期，頁23-33。
4.張英彬(2010)。風速特性與發電量之統計分析。Journal of Nan Kai, Vol.7, No.2 (Special Issue on Gerontechnology), pp.85-94(2010)。
5.莊月璇(2000)。台灣地區風速機率分布研究。未出版之碩士論文，桃園縣，國立中央大學土木工程研究所。
6.中央氣象局西北太平洋颱風列表https：//rdc28.cwb.gov.tw/TDB/public/typhoon_list/
7.中央氣象局颱風的災害與預防
http：//163.28.10.78/content/junior/earth/td_cs/content/clouds/newpage14.htm
英文文獻
8.Alysha M, De Livera, Rob J. Hyndman, Ralph D. Snyder, 2011, Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smooting, Journal of the American Statistical Association, Vol. 106, pp. 1513-1527.
9.Ansley, C. F, Spivey.W. A, Wrobleski. W. J, 1977, A Class of Transformations for Box-Jenkins Seasonal Models, Journal of the Royal Statistical Society Series C(Applied Statistics), Vol. 26, pp. 173-178.
10.Asamoah-Boaheng Michael, 2014, Using SARIMA to Forecast Monthly Mean Surface Air Temperature in the Ashamti Region of Ghana. School of graduate studies research and innovation, Kumasi Polytechnic, Kumasi, Ghana.
11.Box, G.E.P., Cox, D.R., 1964, An Analysis of Transformation, Journal of the Royal Statistical Society. Series B (Methodological), Vol. 26, No. 2, pp. 211-252.
12.Box, G.E.P., Jenkins,G.M., and Reinsel, G.C., 2015, Time series Analysis, Forecasting and Control, 5th ed. New York： Prentice-Hall, pp. 306-310.
13.Carta, J. A. and Ramirez, P., 2007, Analysis of Two-component Mixture Weibull Statistics for Estimation of Wind Speed Distributions, Renewable Energy, Vol. 32, pp. 518-531.
14.Chen, Cathy W. S, Lee, Jack C,1997, On Selecting a Power Transformation in Time-series Analysis. Journal of forecasting(1997), Vol. 16, pp. 343-354.
15.Cryer, Jonathan D., Chan, Kung-Sik, 2008, Time Series Analysis With Application in R, 2nd ed.
16.Dickey David A. and Fuller Wayne A., 1979, Distribution of the Estimators for Autoregressive Time Series With a Unit Root, Journal of the American Statistical Assocaition, Vol. 74, No. 366(Jun. 1979), pp. 427-431.
17.Ernesta Grigonytė and Eglė Butkevičiūtė, 2016, Short-term wind speed forecasting using ARIMA model, ENERGETIKA. 2016 . T. 62. Nr. 1-2., pp. 45-55.
18.Hyndman Rob J., Koehler Anne B., Snyder Ralph D., Grose Simone, 2002, A state framework for automatic forecasting using exponential smoothing methods.
19.Iram Naima, Tripti Maharaa, Ashraf Rahman Idrisib, 2018, Effective Short-Term Forecasting for Daily Time Series with Complex Seasonal Patterns, International Conference on Computational Intelligence and Data Science(ICCIDS 2018).
20.Kwiatkowski Denis, Phillips Peter C.B., Schmidt Peter and Shin Yongcheol, 1992, Testing the null hypothesis of stationarity against the alternative of a unit root, journal of Econometrics 54(1992) North-Holland, pp. 159-178.
21.Newey, Whitney K, West, Kenneth D, 1987, A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix, Econometrica 55(3), pp. 703-708.
22.Nor Hamizah Miswan, Rahaini Mohd Said, Siti Haryanti Hairol Anuar, 2016, ARIMA with Regression Model in Modelling Electricity Load Demand.Faculty of engineering technology, Universiti Teknikal Malaysia Melaka(UTeM), 76100 Durian Tunggal, Melaka, Malaysia.
23.Seguro, J. V and Lambert, T. W., 2000, Modern Estimation of the Parameters of Weibull Wind Speed Distribution for Wind Energy Analysis, Journal of Wind Engineering and Industrial Aerodynamics, 85, pp. 75-84.
24.Wallace John M., Hobbs Peter V., 2006, Atmospheric science an introduction survey, 5th ed., pp. 12-14. |
| Description: | 碩士
國立政治大學
統計學系
107354023 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0107354023 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202001702 |
| Appears in Collections: | [統計學系] 學位論文
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