Short-term Cloud Coverage Prediction Using the ARIMA Time Series Model
Wang Y(王钰); Wang CH(王春恒); Shi CZ(史存召); Xiao BH(肖柏华)
Source PublicationRemote Sensing Letters
2018
Issue3Pages:275-284
Abstract

In view of the important role of cloud coverage on the solar (energy) irradiance, the total cloud coverage prediction based on ground-based 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.

KeywordCloud Coverage Arima Time Series Model
Indexed BySCI
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23637
Collection复杂系统管理与控制国家重点实验室_影像分析与机器视觉
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang Y,Wang CH,Shi CZ,et al. Short-term Cloud Coverage Prediction Using the ARIMA Time Series Model[J]. Remote Sensing Letters,2018(3):275-284.
APA Wang Y,Wang CH,Shi CZ,&Xiao BH.(2018).Short-term Cloud Coverage Prediction Using the ARIMA Time Series Model.Remote Sensing Letters(3),275-284.
MLA Wang Y,et al."Short-term Cloud Coverage Prediction Using the ARIMA Time Series Model".Remote Sensing Letters .3(2018):275-284.
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