干旱区生态环境知识资源中心(Arid): Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions

Arid

DOI	10.32604/cmc.2023.031406
	Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions
	Jamei, Mehdi; Bailek, Nadjem; Bouchouicha, Kada; Hassan, Muhammed A.; Elbeltagi, Ahmed; Kuriqi, Alban; Al-Ansar, Nadhir; Almorox, Javier; El-kenawy, El-Sayed M.
通讯作者	Bailek, N
来源期刊	CMC-COMPUTERS MATERIALS & CONTINUA
ISSN	1546-2218
EISSN	1546-2226
出版年	2023
卷号	74 期号:1 页码:1625-1640
英文摘要	Solar energy represents one of the most important renewable energy sources contributing to the energy transition process. Considering that the observation of daily global solar radiation (GSR) is not affordable in some parts of the globe, there is an imperative need to develop alternative ways to predict it. Therefore, the main objective of this study is to evaluate the performance of different hybrid data-driven techniques in predicting daily GSR in semi-arid regions, such as the majority of Spanish territory. Here, four ensemble-based hybrid models were developed by hybridizing Addi-tive Regression (AR) with Random Forest (RF), Locally Weighted Linear Regression (LWLR), Random Subspace (RS), and M5P. The base algorithms of the developed models are scarcely applied in previous studies to predict solar radiation. The testing phase outcomes demonstrated that the AR-RF models outperform all other hybrid models. The provided models were validated by statistical metrics, such as the correlation coefficient (R) and root mean square error (RMSE). The results proved that Scenario #6, utilizing extraterrestrial solar radiation, relative humidity, wind speed, and mean, maximum, and minimum ambient air temperatures as the model inputs, leads to the most accurate predictions among all scenarios (R = 0.968-0.988 and RMSE = 1.274-1.403 MJ/m2middotd). Also, Scenario #3 stood in the next rank of accuracy for predicting the solar radiation in both validating stations. The AD-RF model was the best predictive, followed by AD-RS and AD-LWLR. Hence, this study recommends new effective methods to predict GSR in semi-arid regions.
英文关键词	Solar radiation prediction random forest locally-weighted linear regression additive regression
类型	Article
语种	英语
开放获取类型	Green Submitted, gold
收录类别	SCI-E
WOS记录号	WOS:000874032400023
WOS关键词	RANDOM SUBSPACE ENSEMBLES
WOS类目	Computer Science, Information Systems ; Materials Science, Multidisciplinary
WOS研究方向	Computer Science ; Materials Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/395784
推荐引用方式 GB/T 7714	Jamei, Mehdi,Bailek, Nadjem,Bouchouicha, Kada,et al. Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions[J],2023,74(1):1625-1640.
APA	Jamei, Mehdi.,Bailek, Nadjem.,Bouchouicha, Kada.,Hassan, Muhammed A..,Elbeltagi, Ahmed.,...&El-kenawy, El-Sayed M..(2023).Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions.CMC-COMPUTERS MATERIALS & CONTINUA,74(1),1625-1640.
MLA	Jamei, Mehdi,et al."Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions".CMC-COMPUTERS MATERIALS & CONTINUA 74.1(2023):1625-1640.