[1]
World Wind Energy Report 2008, World Wind Energy Association WWEA, (2009).
Google Scholar
[2]
World Energy Outlook 2009, International Energy Agency, (2009).
Google Scholar
[3]
Zhou Yan-li, Wind Power's Current Situation And Development Trend, Gansu Science and Technology, vol. 24, Feb. 2008, pp.9-11.
Google Scholar
[4]
Huang Dong, Li Huai-xia, Zhang Zhen, Wind Power Industry: Global Situation and Government Policies, Power System and Clean Energy, vol. 25, Feb. 2009, pp.46-49.
Google Scholar
[5]
Alexandre Costa, Antonio Crespo, Jorge Navarro, Gil Lizcano, Henrik Madsen, Everaldo Feitosa, A Review on the Young History of the Wind Power Short-term Prediction, Renewable and Sustainable Energy Reviews, vol. 12, Dec. 2008, pp.1725-1744.
DOI: 10.1016/j.rser.2007.01.015
Google Scholar
[6]
Ismael Sanchez, Short-term Prediction of Wind Energy Production, International Journal of Forecasting, vol. 22, May. 2006, pp.43-56, doi: 10. 1016/j. ijforecast. 2005. 05. 003.
Google Scholar
[7]
Wang Li-jie, Liao Xiao-zhong, Gao Yang, Gao Shuang, Summarization of Modeling and Prediction of Wind Power Generation, Power System Protection and Control, vol. 37, July. 2009, pp.118-121.
Google Scholar
[8]
Gao Yang, Chen Hua-yu, Ou Yang-qun, Summarization Of Forecasting Technology of Wind Power Generation, Power System and Clean Energy, vol. 26, Apr. 2010, pp.60-63.
Google Scholar
[9]
Han Shuang, Study of Short-term Wind Power Prediction Method, Beijing: North China Electric Power University, (2008).
Google Scholar
[10]
Ding Qiao-lin, Pna Xue-hua, Yang Xue-ming, Optimal Combined Forecasting Method Used in Power Load Forecasting, Power System Technology, vol. 32, Jun. 2008, pp.127-130.
Google Scholar
[11]
Jian Dan, Information Theory & Coding, 1st ed. Hefei: University of Science and Technology of China Press. 2001, pp.10-54.
Google Scholar
[12]
Shi Feng, Mu Zhong-xi, Foundation of Information Theory, 1st ed. Wuhan: Wuhan University Press, 2006, pp.255-259.
Google Scholar
[13]
Zhu Cheng-qi, Sun Hong-bin, Zhang Bo-ming, A Combined Model for Short-term Load Forecasting Based on Maximum Entropy Principle, Proceedings of the CSEE, vol. 15, Oct. 2005, pp.1-6.
Google Scholar
[14]
Qu Zhao-wei, Yao Rong-han, Wang Dian-hai, A Combine Model for Inhabits Trip Distribution Forecasting Based on Maximum Entropy Pinciple, Journal of Jilin University (Engineering and Technology Edition), vol. 33, Apr. 2003, pp.15-19.
Google Scholar
[15]
Wang Xiao-long, Yuan Zhi-fa, Guo Man-Cai, Song Shi-de, Zhang Quan-qi, Bao Zhen-min. Maximum Entropy Principle and Population Genetic Equilibrium [J]. Acta Genetica Sinica, vol. 19, pp.562-564., (2002).
Google Scholar
[16]
Gao Feng, Kang Chong-qing, Xia Qing, Huang Yong-hao, Shang Jin-Chen, Meng Yuan-jing, He Nan-qiang, Methods of Automatic selection in Load Forecating Models, Automation of Electric Power Systems, vol. 28, Mar. 2004, pp.11-13.
Google Scholar
[17]
Hu Yu-da, Nonlinear Programming, 2ed ed. Beijing: Higher Education Press,. 1990, pp.63-79.
Google Scholar
[18]
Yang Xiu-yuan, Xiao Yang, Chen Shu-yong, Wind Speed and Generated Power Forecasting in Wind Farm, Proceedings of the CSEE, vol. 25, Jun. 2005, pp.1-5.
Google Scholar
[19]
Pan Di-fu, Liu Hui, Li Yan-fei, A Wind Speed Forecasting Optimization Model for Wind Farms Based on Time Series Analysis and Kalman Filter Algorith, Power System Technology, vol. 32, Apr. 2008, pp.82-86.
Google Scholar