Abstract：To improve the accuracy of wind speed forecasting, a method based on relevant vector machine(RVM) and auto-regressive moving average(ARMA) error correcting is proposed. Firstly, the nonlinear model of influencing factors and wind speed of the next 24 hours are built based on RVM, and genetic algorithm(GA) is used to ensure the optimization of model parameters. Secondly, the error series of wind speed forecasted by RVM model are adjusted by ARMA model. Finally, the forecasted wind speed is amended by the forecasting errors, which are generated by ARMA model. The wind speed forecasting results of any day in the future for a wind farm in the Jiangsu province demonstrate that the proposed method is reasonable and effective.
孙国强, 卫志农, 翟玮星. 基于RVM与ARMA误差校正的短期风速预测[J]. 电工技术学报, 2012, 27(8): 187-193.
Sun Guoqiang, Wei Zhinong, Zhai Weixing. Short Term Wind Speed Forecasting Based on RVM and ARMA Error Correcting. Transactions of China Electrotechnical Society, 2012, 27(8): 187-193.
[1] 韩爽. 风电场功率短期预测方法研究[D]. 北京: 华北电力大学, 2008. [2] 杨秀媛, 肖洋, 陈树勇. 风电场风速和发电功率预测研究[J]. 中国电机工程学报, 2005, 25(11): 1-5. Yang Xiuyuan, Xiao Yang, Chen Shuyong. Wind speed and generated power forecasting in wind farm[J]. Proceedings of the CSEE, 2005, 25(11): 1-5. [3] 孙春顺, 王耀南, 李欣然. 小时风速的向量自回归模型及应用[J]. 中国电机工程学报, 2008, 28(14): 112-117. Sun Chunshun, Wang Yaonan, Li Xinran. A vector autoregression model of hourly wind speed and its applications in hourly wind speed forecasting[J]. Proceedings of the CSEE, 2008, 28(14): 112-117. [4] 丁明, 张立军, 吴义纯. 基于时间序列分析的风电场风速预测模型[J]. 电力自动化设备, 2005, 25(8): 32-34. Ding Ming, Zhang Lijun, Wu Yichun. Wind speed forecast model for wind farms based on time series analysis[J]. Electric Power Automation Equipment, 2005, 25(8): 32-34 [5] 潘迪夫, 刘辉, 李燕飞. 风电场风速短期多步预测改进算法[J]. 中国电机工程学报, 2008, 28(26): 87-91. Pan Difu, Liu Hui, Li Yanfei. Optimization algorithm of short-term multi-step wind speed forecast[J]. Proceedings of the CSEE, 2008, 28(26): 87-91. [6] Kariniotakis G, Stavrakakis G, Nogaret E. Wind power forecasting using advanced neural network models[J]. IEEE Transactions on Energy Conversion, 1996, 11(4): 762-767. [7] 范高锋, 王伟胜, 刘纯, 等. 基于人工神经网络的风电功率预测[J]. 中国电机工程学报, 2008, 28(34): 118-123. Fan Gaofeng, Wang Weisheng, Liu Chun, et al. Wind power prediction based on artificial neural network[J]. Proceedings of the CSEE, 2008, 28(34): 118-123. [8] 李文良, 卫志农, 孙国强, 等. 基于改进空间相关法和径向基神经网络的风电场短期风速分时预测模型[J]. 电力自动化设备, 2009, 29(6): 89-92. Li Wenliang, Wei Zhinong, Sun Guoqiang, et al. Multi-terval wind speed forecast model based on improved spatial correlation and RBF neural network[J]. Electric Power Automation Equipment, 2009, 29(6): 89-92. [9] 张国强, 张伯明. 基于组合预测的风电场风速及风电机功率预测[J]. 电力系统自动化, 2009, 33(18): 92-96. Zhang Guoqiang, Zhang Boming. Wind speed and wind turbine output forecast based on combination method[J]. Automation of Electric Power Systems, 2009, 33(18): 92-96. [10] Michael E Tipping. Sparse Bayesian learning and the relevance vector machine[J]. Journal of Machine Learning Research, 2001, 1(3): 211-244. [11] 刘遵雄, 张德运, 孙钦东, 等. 基于相关向量机的电力负荷中期预测[J]. 西安交通大学学报, 2004, 38(10): 1005-1008. Liu Zunxiong, Zhang Deyun, Sun Qindong, et al. Mid-term electric load prediction based on the relevant vector machine[J]. Journal of Xi’an Jiaotong University, 2004, 38(10): 1005-1008. [12] Joaquin Quinonero-Candela, Lars Kai Hansen. Time series prediction based on the relevance vector machine with adaptive kernels[C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, Hong Kong, China, 2002, 1: 985-988. [13] 刘芳, 周建中, 邱方鹏, 等. 基于相关向量回归的非线性时间序列预测方法[J]. 计算机工程, 2008, 34(3): 1-5. Liu Fang, Zhou Jianzhong, Qiu Fangpeng, et al. Nonlinear time series forecasting based on relevance vector regression[J]. Computer Engineering, 2008, 34(3): 1-5. [14] 牛林, 杜至刚, 赵建国. 基于相关向量机的交流特高压输电线路可听噪声的预测研究[J]. 电力自动化设备, 2009, 29(6): 71-76. Niu Lin, Du Zhigang, Zhao Jianguo. Prediction of audible noise of AC UHV transmission lines based on relevance vector machine[J]. Electric Power Automation Equipment, 2009, 29(6): 71-76. [15] Shian-Chang Huang, Tung-Kuang Wu. Wavelet-based relevance vector machines for stock index forecasting[C]. 2006 International Joint Conference on Neural Networks, 2006: 603-609. [16] 王炜, 郭小明, 王淑艳, 等. 关于核函数选取的方法[J]. 辽宁师范大学学报(自然科学版), 2008, 31(1): 1-4. Wang Wei, Guo Xiaoming, Wang Shuyan, et al. On the method of kernel-function selection in support vector machine[J]. Journal of Liaoning Normal University(Natural Science Edition), 2008, 31(1): 1-4. [17] 王丽婕, 廖晓钟, 高阳, 等. 风电场发电功率的建模和预测研究综述[J]. 电力系统保护与控制, 2009, 37(13): 118-121. Wang Lijie, Liao Xiaozhong, Gao Yang, et al. Summarization of modeling and prediction of wind power generation[J]. Power System Protection and Control, 2009, 37(13): 118-121. [18] 王燕. 应用时间序列[M]. 北京: 中国人民大学出版社, 2005.
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