摘要
采用Elman神经网络模型预估海口逐时太阳总辐射,并针对神经网络算法上存在泛化能力弱,提高训练精度困难,训练时间长等问题,将灰色关联分析法引入太阳总辐射预测模型旨在构建拟合精度优的模型.利用2009~2019年海口气象站逐时太阳总辐射曝辐量数据以及影响总辐射的气压、气温、相对湿度、降水量和日照等气象数据,分别构建基于Elman神经网络和基于相似日的Elman神经网络逐时太阳总辐射预测模型,其中2009~2018年为测试样本集,2019年数据作为检验数据集,并利用空间插值方法研究琼北地区辐射时空分布特征.结果表明,经过相似日筛选的辐射预测模型与观测值相关系数达0.97,平均相对误差在±0.5 MJ·m-2范围内,模型预测精度优于Elman神经网络算法;琼北总辐射整体空间分布均匀,具体呈现南多北少特征;时间分布上总辐射呈现冬季低夏季高,春秋季居中的特征.
In our report,the Elman neural network model was used to predict the hourly global solar radiation in Haikou,and aimed at the shortcomings of the neural network algorithm such as difficulty in the ability of generalization,training approximation,and long training time,a solar total radiation prediction model combined with the gray correlation analysis method was established.Hourly irradiation exposure of global radiation data at the Haikou Weather Station in 2009~2019,and meteorological data such as atmospheric pressure,temperature,relative humidity,precipitation,and sunshine that affect the global radiation were used,the hourly solar radiation prediction model based on Elman neural network with or without similar day was constructed respectively,and for which the data in 2009~2018 were used as the training data set,and the data in 2019 were used as the validation data set,a study of the temporal and spatial distribution characteristics of radiation in Qiongbei area using the spatial interpolation method was performed.The results showed that the correlation coefficient of the global radiation prediction model and the observation value after the similar day screening reaches 0.97,the average relative error is within±0.5 MJ·m-2,and the accuracy of this prediction model is better than that of the Elman neural network algorithm.The spatial distribution was even,showing the characteristics of much south and less north.And the temporal distribution of global radiation in Qiongbei area was less in winter and was the highest in summer,and was centered in spring and autumn.
作者
王小洁
陈珍莉
王旭
施晨晓
刘霄燕
Wang Xiaojie;Chen Zhenli;Wang Xu;Shi Chenxiao;Liu Xiaoyan(Meteorological Information Center of Hainan Province, Key Laboratory of South China Sea Meteorological Disaster Preventionand Mitigation of Hainan Province, Haikou 570203, China)
出处
《海南大学学报（自然科学版）》
CAS
2020年第4期347-355,共9页
Natural Science Journal of Hainan University
基金
海南省气象局青年基金(HNQXQN201801)。
关键词
太阳总辐射
灰色关联法
相似日
ELMAN神经网络
global solar radiation
grey correlation method
similarity day
Elman neural network
