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Study of Short-Term Wind Power Forecasting Technology Based on a Combinational Model
|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:|| L. Landberg, “A Mathematical Look at a Physical Power Prediction Model,” Wind Energy, Vol. 1, No. 1, pp. 23-28, September 1998.  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.  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.  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.  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.  H. Wold, “A Study in the Analysis of Stationary Time Series,” Stockholm, Sweden: Almgrist & Wiksell, 1938.  G.E.P. Box and G. Jenkins, “Time Series Analysis, Forecasting and Control,” San Francisco, CA: Holden-Day, 1970.  G.E.P. Box, G.M. Jenkins, and G.C. Reinsel, “Time Series Analysis, Forecasting and Control,” Englewood Cliffs, NJ: Prentice-Hall, 1994.  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.  V. Vapnik, “The Nature of Statistical Learning Theory,” New York: Springer-Verlag, 2000.  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.  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.  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.  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.  S.S. Haykin, “Neural Networks: A Comprehensive Foundation,” New York: Macmillan, 1994.  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.  M. Sugeno, “Industrial applications of fuzzy control,” Elsevier Science Pub. Co., 1985.  J. Holland, “Adaptation in Natural and Artificial Systems,” Ann Arbor, MI: Univ. Michigan Press, 1976.  D.E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning,” Reading, MA: Addison-Wesley, 1989.  R. Serfling, “Approximately Optimum Stratification,” Journal of the American Statistical Association, Vol. 63, pp. 1298-1309, 1968.  R. Singh and B.V. Sukhatme, “Optimum Stratification,” Annals ofInstitute of Statistical Mathematics, Vol. 21, pp. 515-528, 1969.  R. Singh, “Approximately Optimum Stratification on theAuxiliary Vaiable,” Journal of the American Statistical Mathematics, Vol. 66, pp. 829-833, 1971.||摘要:||
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.
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