主办单位：中国气象局沈阳大气环境研究所
国际刊号：ISSN 1673-503X
国内刊号：CN 21-1531/P
气象与环境学报

气象与环境学报 ›› 2013, Vol. 29 ›› Issue (3): 85-91.doi:

• 论文 • 上一篇    下一篇

两种太阳总辐射分钟级预报方法的比较

江滢1,2  申彦波1,2  党军3   

  1. 1.中国气象局风能太阳能资源中心，北京 100081；2.中国气象局公共气象服务中心，北京 100081；3.吐鲁番市气象局，新疆 吐鲁番 838000
  • 出版日期:2013-06-29 发布日期:2013-06-29

Comparison on two prediction methods of minutely global solar radiation

JIANG Ying1,2 SHEN Yan-bo1,2 DANG Jun3   

  1. 1. Center for Wind and Solar Energy Resources, China Meteorological Administration, Beijing 100081, China; 2. Public Weather Service Center, China Meteorological Administration, Beijing 100081, China; 3. Turfan Meteorological Service, Turfan 838000, China
  • Online:2013-06-29 Published:2013-06-29

PDF (PC)

393

被引次数

6

摘要： 利用2010年9月1月至2011年8月31日中国气象局风能太阳能资源中心吐鲁番太阳能试验站水平太阳总辐射逐分钟观测资料，对统计外推法（简称外推法，TRD）和动态神经网络法（简称神经网络法，NNN）两种太阳总辐射分钟级预报方法进行比较。结果表明：从全年平均角度来看，外推法略优于神经网络法。两种预报方法的预报效果都主要依赖于天气形势，晴天预报效果最好，阴雨天气次之，多云和扬沙天气预报效果较差。外推法能较为准确地反映起报时刻影响辐射观测的云、气溶胶、沙尘等状况，并将这种影响持续至滚动预报的第一步或第二步；而神经网络法在预报阴雨、多云等复杂天气形势下预报要素的非趋势性变化规律方面略优；外推法预报日出后3 h和日落前3 h的太阳总辐射变化明显优于神经网络法；而神经网络法预报太阳总辐射突然变化特征时略优于外推法。

关键词: 水平太阳总辐射, 逐分钟预测, 统计外推, 自适应神经网络

Abstract: Using the minutely global solar radiation data from September 1, 2010 to August 31, 2011 at the Turpan solar energy station of center for wind and solar energy resources of China Meteorological Administration (CMA), two prediction methods of minutely global solar radiation were compared, namely, a statistical extrapolation (TRD) method and a dynamic neural network (NNN) method. The results show that for the annual average, the prediction effect is better by the TRD than by the NNN. The prediction accuracy rates of the two methods is related with weather situation, and it is higher in a sunny day than in a rainy day, while it is lower in a cloud day and a sand-storm day. The impact factors of radiation observation such as cloud, aerosol and dust and so on could be accurately expressed by the TRD method at the beginning time of prediction, which could be continued to the first or second step of rolling forecasts. The prediction effect is better by the NNN than by the TRD for the non-linear variation of global solar radiation under complex weather such as rainy day and cloud day. The predication accuracy of global solar radiation is higher by the TRD than by the NNN for three hours after sunrise and before sunset. The prediction effect is slightly better by the NNN than by the TRD for a sudden variation of global solar radiation.

Key words: Global solar radiation, Minutely prediction, Statistical extrapolation, Dynamic neural network