Short-term cloud coverage prediction using the ARIMA time series model
Short-term cloud coverage prediction using the ARIMA time series model
复制标题
使用 ARIMA 时间序列模型进行短期云覆盖预测
DOI：
10.1080/2150704x.2017.1418992
复制
发表时间：
2018-01-01
影响因子：
2.3
通讯作者：
Xiao, Baihua
中科院分区：
文献类型：
--
作者：
Wang, Yu;Wang, Chunheng;Xiao, Baihua
鉴于云量对太阳(能量)辐照度的重要影响，本文研究了基于地面云图的总云量预测方法。在传统的预报技术中，云覆盖率在连续时间内的相关性往往被忽略。为此，采用自回归积分移动平均(ARIMA)时间序列模型对短时云量进行预测。在收集的地面云图云量时间序列数据库上的实验结果表明，时间序列的相关性信息对云量预测是有用的。此外，ARIMA模型对1分钟或更长时间的20分钟和30分钟的预测获得了更好的预测性能。对于1分钟、5分钟、20分钟和30分钟的预测，我们能够预测云的覆盖率，近似误差分别为5%、7%和9%。此外，我们还发现，对于不同的云覆盖间隔，预测的错误率是不同的。高云覆盖率总是伴随着更高的错误率。
In view of the important role of cloud coverage on the solar (energy) irradiance, the total cloud coverage prediction based on groundbased cloud images is studied in this paper. In traditional prediction techniques, the correlation between cloud coverage over continue time is always neglected. Thus, an autoregressive integrated moving average (ARIMA) time series model is used to predict the short-term cloud coverage. Experimental results on a collected time series database of cloud coverage computed from ground-based cloud images show that the correlation information of time series is useful for cloud coverage prediction. Additionally, the ARIMA model gains a superior prediction performance for forecasts of one minute or longer 20 and 30 minutes. We are able to predict the cloud coverage with an approximate error of 5%, 7%, and 9% for 1, 5, and 20 and 30 minute forecasts, respectively. Furthermore, we found that there are different error rates of predictions for different cloud coverage intervals. High cloud coverage always suffers from a higher error rate.