Performance evaluation and accuracy enhancement of a day-ahead wind power forecasting system in China
Performance evaluation and accuracy enhancement of a day-ahead wind power forecasting system in China
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DOI：
10.1016/j.renene.2011.11.051
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发表时间：
2012-07
期刊：
影响因子：
8.7
通讯作者：
Pan Zhao;Jiangfeng Wang;Junrong Xia;Yiping Dai;Yingxin Sheng;Jie-shun Yue
中科院分区：
文献类型：
--
作者：
Pan Zhao;Jiangfeng Wang;Junrong Xia;Yiping Dai;Yingxin Sheng;Jie-shun Yue
建立风电功率预测系统有助于提高风能利用率。最新数据显示，中国已成为全球最大的风能市场。然而，在中国，很少有研究介绍风能预测技术。本文介绍了一种新的日前风电功率预测系统在中国的性能评估和精度提高。该系统由数值天气预报模式和人工神经网络组成。数值预报模式是将全球预报系统（GFS）和天气研究与预报系统（WRF）耦合起来，预报气象参数的模式。此外，该系统还集成了卡尔曼滤波器，以减小WRF风速预报的系统误差，提高预报精度。真实的世界案例的数值结果证明了该预测系统在原始风速修正和风功率预测精度方面的有效性。归一化均方根误差（NRMSE）具有16.47%的月平均值，这是允许在电力市场操作中使用预测值的可接受的误差容限。该预测系统对提高我国风能普及率具有一定的参考价值。
Wind power forecasting system is useful to increase the wind energy penetration level. Latest statistics show that China has been the biggest wind energy market throughout the world. However, few studies have been published to introduce the wind energy forecasting technologies in China. This paper presents the performance evaluation and accuracy enhancement of a novel day-ahead wind power forecasting system in China. This system consists of a numerical weather prediction (NWP) model and artificial neural networks (ANNs). The NWP model is established by coupling the Global Forecasting system (GFS) with the Weather Research and Forecasting (WRF) system together to predict meteorological parameters. In addition, Kalman filter has been integrated in this system to reduce the systematic errors in wind speed from WRF and enhance the forecasting accuracy. The numerical results from a real world case are proven the effectiveness of this forecasting system in terms of the raw wind speed correction and wind power forecasting accuracy. The Normalized Root Mean Square Error (NRMSE) has a month average value of 16.47%, which is an acceptable error margin for allowing the use of the forecasted values in electric market operations. This forecasting system is profitable for increasing the wind energy penetration level in China.