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| 基于最优输入径向基网络的风电功率预测方法 | |
| 引用本文： | 赵宏伟,王媛媛,曾瑛,颜少凌.基于最优输入径向基网络的风电功率预测方法[J].电力科学与技术学报,2014(4):60-64. |
| 作者姓名： | 赵宏伟 王媛媛 曾瑛 颜少凌 |
| 作者单位： | 1. 广州供电局 电力调度控制中心,广东 广州,510620 2. 长沙理工大学 电力系统安全运行与控制湖南省高校重点实验室,湖南 长沙,410004 3. 娄底供电公司,湖南 娄底,417000 4. 福建中闽能源投资有限责任公司,福建 福州,350003 |
| 基金项目： | 国家自然科学基金，湖南省自然科学基金 |
| 摘 要： | 随着风电的大规模接入电网,对风电功率未来出力的把握显得尤为重要,而风电功率预测技术则是掌握出力特性的有力工具。基于实测历史数据,研究系统不同输入量对预测结果误差的影响,选取最佳输入量值;并在此基础上,构建基于RBF（径向基）神经网络的风电功率预测模型,对风电功率进行有效预测;预测结果表明,基于径向基神经网络的预测方法预测精度较高,可以为电网提供更加准确的风电预测出力信息,有助于为调度制定更加合理有效的计划。 |
| 关 键 词： | 风电功率预测 人工智能法 RBF神经网络 调度计划 |
A wind power prediction method based on RBF neural network with optimal input |
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| ZHAO Hong-wei,WANG Yuan-yuan,ZENG Ying,YAN Shao-ling.A wind power prediction method based on RBF neural network with optimal input[J].JOurnal of Electric Power Science And Technology,2014(4):60-64. | |
| Authors: | ZHAO Hong-wei WANG Yuan-yuan ZENG Ying YAN Shao-ling |
| Affiliation: | ZHAO Hong-wei, WANG Yuan-yuan, ZENG Ying, YAN Shao-ling ( 1.Dispatching ＆ Control Center, Guangzhou Power Supply Bureau, Guangzhou 510620, China; 2. Hunan Province Higher Education Key Laboratory of Power System Safety Operation and Control,Changsha University of Science and Technology, Changsha 410004, China; 3.Loudi Electric Power Corporation, Loudi 417000, China;4. Fujian Zhongmin Energy Investment Co. Ltd., Fuzhou 350003, China) |
| Abstract: | With large-scale integration of wind power in power grids,it is of great importance to grasp the characteristics of future wind power output.Wind power forecasting is a useful tool to investigate the characteristics.Based on the historical data,this paper investigated the influence of different system input on the predicting error in order to get the best input values,and then constructed a wind power prediction model based on RBF （radial basis function）neural network. The prediction results showed that the wind power forecasting method based on the RBF neural network hadhigh precision.The results can provide more accurate information of wind power future output for the power system.The proposed power prediction method can be used to make more reasonable dispatching plans. |
| Keywords: | wind power prediction artificial intelligence method RBF neural network dispatc-hing plans |
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