Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/6509
標題: 基於組合模型之短期風力預測技術研究
Study of Short-Term Wind Power Forecasting Technology Based on a Combinational Model
作者: 楊浩德
Yang, Hau-De
關鍵字: 風力發電;Wind power;自回歸整合移動平均模式;支持向量機;倒傳遞類神經網路;模糊類神經網路推論系統;基因演算法;Auto-regressive integrated moving average with extra;Support vector machine;Feed-forward back-propagation neural network;Adaptive neural fuzzy inference system;Genetic algorithm
出版社: 電機工程學系所
引用: [1] L. Landberg, “A Mathematical Look at a Physical Power Prediction Model,” Wind Energy, Vol. 1, No. 1, pp. 23-28, September 1998. [2] J.S. Hong, “Evaluation of the high-resolution model forecasts over the Taiwan area during GIMEX,” Weather and Forecasting, Vol. 18, pp. 836-846, 2003. [3] C. Erasmo and R. Wilfrido, “Wind speed forecasting in the South Coast of Oaxaca, Mexico,”Renewable Energy, Vol. 32, No. 12, pp. 2116-2128, 2007. [4] P. Chen, T. Pedersen, B. Bak-Jensen, and Z. Chen, “ARIMA-based time series model of stochastic wind power generation,” IEEE Transactions on Power Systems, Vol. 25, No. 2, pp. 667-676, 2010. [5] G.U. Yule, “Why do we sometimes get nonsense-correlations between time series? A study in sampling and the nature of time series,” J. R.Statist. Soc., Vol. 89, pp. 1-64, 1926. [6] H. Wold, “A Study in the Analysis of Stationary Time Series,” Stockholm, Sweden: Almgrist & Wiksell, 1938. [7] G.E.P. Box and G. Jenkins, “Time Series Analysis, Forecasting and Control,” San Francisco, CA: Holden-Day, 1970. [8] G.E.P. Box, G.M. Jenkins, and G.C. Reinsel, “Time Series Analysis, Forecasting and Control,” Englewood Cliffs, NJ: Prentice-Hall, 1994. [9] J.C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Min. Knowl. Discov., Vol. 2, No. 2, pp. 121-167, June 1998. [10] V. Vapnik, “The Nature of Statistical Learning Theory,” New York: Springer-Verlag, 2000. [11] B. Ribeiro, “Support vector machines for quality monitoring in a plastic injection molding process,” IEEE Trans. Syst., Man, Cybern. C, Appl.Rev., Vol. 35, No. 3, pp. 401-410, August 2005. [12] J.I. Park, S.H. Baek, M.K. Jeong, and S.J. Bae, “Dual features functional support vector machines for fault detection of rechargeable batteries,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., Vol. 39, No. 4, pp. 480-485, July 2009. [13] J.A.K. Suykens and J. Vandewalle, “Least squares support vector machine classifiers,” Neural Process. Lett., Vol. 9, No. 3, pp. 293-300, June 1999. [14] J.A.K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, and J. Vandewalle, “Least Squares Support Vector Machines,” Singapore: World Scientific, 2002. [15] S.S. Haykin, “Neural Networks: A Comprehensive Foundation,” New York: Macmillan, 1994. [16] J.S.R. Jang, “ANFIS: Adaptive-network-based fuzzy inference system,” IEEE Trans. Syst., Man, Cybern., Vol. 23, No. 3, pp. 665-685, May- June 1993. [17] M. Sugeno, “Industrial applications of fuzzy control,” Elsevier Science Pub. Co., 1985. [18] J. Holland, “Adaptation in Natural and Artificial Systems,” Ann Arbor, MI: Univ. Michigan Press, 1976. [19] D.E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning,” Reading, MA: Addison-Wesley, 1989. [20] R. Serfling, “Approximately Optimum Stratification,” Journal of the American Statistical Association, Vol. 63, pp. 1298-1309, 1968. [21] R. Singh and B.V. Sukhatme, “Optimum Stratification,” Annals ofInstitute of Statistical Mathematics, Vol. 21, pp. 515-528, 1969. [22] R. Singh, “Approximately Optimum Stratification on theAuxiliary Vaiable,” Journal of the American Statistical Mathematics, Vol. 66, pp. 829-833, 1971.
摘要: 
近來隨著石化原料價格高漲和全球暖化的問題日益嚴重,以及京都議定書的生效,世界各國積極的開發再生能源。在諸多的再生能源中,風力發電憑藉其綠色環保、資源豐富、容易開發等優勢,得到世界各國地廣泛重視,是目前世界上發展得最快的再生能源。然而風力發電的最大障礙,來自其天然的多變性,使得電網在系統整合方面或是各發電機組間的調度產生相當大的困難。這種先天性的障礙,若能透過準確的風力預測,當可將電力系統運轉或是系統調度等困難減至最低。
本論文使用單一預測法和組合預測法,搭配分層理論的分段概念來預測短期風力輸出,單一預測法分別應用自回歸整合移動平均模式(ARIMAX)、支持向量機(SVM)、倒傳遞類神經網路(BP-ANN)及模糊類神經網路推論系統(ANFIS)建立風力的預測模型,組合預測法應用基因演算法(GA)建立風力的預測模型,風力資料庫是澎湖地區中屯風場4組不同時段的風力資料,經本文的預測模型預測後,其預測結果均顯示組合預測比單一預測準確,因為GA組合預測能有效突顯各單項預測在不同分段區間中的權重值分配,並且強化整體預測結構,有了此預測架構,未來將預測台灣其他風場的輸出,並比較不同風場個別適用的預測模型,也能將此預測資料提供電力公司作為經濟調度。

With the hiking prices of fossil raw materials, mounting gravity of global warming, and the implementation of the Kyoto Protocol, countries all over the world have worked with great vitality to develop renewable energies in recent years. Of all currently available renewable energies, wind power has received extensive attention thanks to its environmental friendliness, virtually inexhaustible abundance, and relatively simplicity in development. However, though quickly becoming the fastest-growing form of renewable energy, wind power finds its disadvantage in the inherent changeability and instability of wind resources that have caused considerable difficulties in wind farm and grid integration and dispatch of power generators. However, the impacts of this inherent disadvantage on power system operation and dispatch can be minimized with accurate wind power forecasts.
The thesis adopts four stand-alone and develops one hybrid model and applies the concept of segmentation in the theory of optimal stratification to forecast short-term wind power outputs. auto-regressive integrated moving average with extra (ARIMAX), support vector machine (SVM), feed-forward back-propagation neural network (BP-ANN), and adaptive neural fuzzy inference system (ANFIS) are used to construct stand-alone wind power forecasting models respectively while genetic algorithm (GA) is used to construct the hybrid forecasting model. Construction of wind power forecasting models is based on the wind power databases during four different periods at the Zhongtun Wind Farm in Penghu, Taiwan. As demonstrated by the forecasting results, the hybrid model outperforms all stand-alone models in terms of forecast accuracy due to its ability to highlight the distribution of weighted values in different segmentation blocks in one single forecast and to reinforce the overall structure of the forecasting model. The models adopted and developed by the study can also be applied to forecast the outputs of other wind farms and to compare the compatibility of various forecast models with individual wind farms. Research results of the thesis can provide power companies with important reference for expediting economic dispatch.
URI: http://hdl.handle.net/11455/6509
其他識別: U0005-1508201121402800
Appears in Collections:電機工程學系所

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