Short-term cloud coverage prediction using the ARIMA time series model
Wang, Yu1,2,3; Wang, Chunheng1; Shi, Cunzhao1; Xiao, Baihua1
Source PublicationREMOTE SENSING LETTERS
2018
Volume9Issue:3Pages:274-283
SubtypeArticle
AbstractIn 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.
WOS HeadingsScience & Technology ; Technology
DOI10.1080/2150704X.2017.1418992
WOS KeywordPOINT CLOUDINESS ; SKY IMAGER ; CLASSIFICATION ; SEGMENTATION ; OPTIMIZATION ; IRRADIANCE ; SATELLITE ; RADIATION ; SURFACE
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China (NSFC)(61531019 ; 61503228 ; 61601462 ; 71621002)
WOS Research AreaRemote Sensing ; Imaging Science & Photographic Technology
WOS SubjectRemote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000428631900009
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22008
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
Affiliation1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
3.Shanxi Univ, Sch Software, Taiyuan, Shanxi, Peoples R China
Recommended Citation
GB/T 7714
Wang, Yu,Wang, Chunheng,Shi, Cunzhao,et al. Short-term cloud coverage prediction using the ARIMA time series model[J]. REMOTE SENSING LETTERS,2018,9(3):274-283.
APA Wang, Yu,Wang, Chunheng,Shi, Cunzhao,&Xiao, Baihua.(2018).Short-term cloud coverage prediction using the ARIMA time series model.REMOTE SENSING LETTERS,9(3),274-283.
MLA Wang, Yu,et al."Short-term cloud coverage prediction using the ARIMA time series model".REMOTE SENSING LETTERS 9.3(2018):274-283.
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