| 基于NWP和改进神经网络的短啦风电功率预测研究 |
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| 引用本文： | 邱道尹,宋慧娟,田芳.基于NWP和改进神经网络的短啦风电功率预测研究[J].人天科学研究,2013(4):31-33. |
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| 作者姓名： | 邱道尹 宋慧娟 田芳 |
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| 作者单位： | 华北水利水电学院,河南郑州450011 |
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| 摘 要： | 风电场功率预测对电力系统稳定运行起着决定性作用。首先对传统BP神经网络进行改进，以某一风电场获取的2月1日-10日的天气预报（NWP）数据和功率数据作为改进后BP神经网络的训练数据，对神经网络进行训练；其次以2月11号3小时的数值天气预报数据作为改进后BP神经网络的输入数据，对未来3小时的输出功率进行预测。预测过程和结果显示，改进后的BP神经网络在满足低预测误差的同时，能够提高BP神经网络的稳定性和收敛速度。
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| 关 键 词： | 风电场功率预测 天气预报（NWP） BP神经网络 预测误差 |
Study of Wind Power Short-Term Prediction of Wind Farm Based on NWP and Neural Network |
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| Abstract: | Wind power prediction of wind farm is plays a decisive role in stable electric power system operation. Firstly to improve the traditional BP neural network, to use the numerical weather prediction data and power data, from feb. 01 to 10, as the training data of the improved BP neural network; secondly to use 3 hours data about numerical weather prediction of feb. 11 as inputting data, to predict The next 3 hours of output power. Process and result of prediction displays, the improved BP neural network ensures lower prediction error, at the same time, it can improves the stability of BP neural networks and the rate of convergence. |
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| Keywords: | Wind Power Prediction of Wind Farm Numerical Weather Prediction BP Neural Network Prediction Error |
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