干旱区生态环境知识资源中心(Arid): Hourly predictions of direct normal irradiation using an innovative hybrid LSTM model for concentrating solar power projects in hyper-arid regions

Arid

DOI	10.1016/j.egyr.2022.10.402
	Hourly predictions of direct normal irradiation using an innovative hybrid LSTM model for concentrating solar power projects in hyper-arid regions
	Djaafari, Abdallah; Ibrahim, Abdelhameed; Bailek, Nadjem; Bouchouicha, Kada; Hassan, Muhammed A.; Kuriqi, Alban; Al-Ansar, Nadhir; El-Kenawy, El-Sayed M.
通讯作者	Bailek, N
来源期刊	ENERGY REPORTS
ISSN	2352-4847
出版年	2022
卷号	8 页码:15548-15562
英文摘要	Although solar energy harnessing capacity varies considerably based on the employed solar energy technology and the meteorological conditions, accurate direct normal irradiation (DNI) prediction remains crucial for better planning and management of concentrating solar power systems. This work develops hybrid Long Short-Term Memory (LSTM) models for assessing hourly DNI using meteorological datasets that include relative humidity, air temperature, and global solar irradiation. The study proposes a unique hybrid model, combining a balance-dynamic sine-cosine (BDSCA) algorithm with an LSTM predictor. Combining optimizers and predictors, such hybrid models are rarely developed to estimate DNI, especially in smaller prediction intervals. Therefore, various commonly adopted algorithms in relevant studies have been considered references for evaluating the new hybrid algorithm. The results show that the relative errors of the proposed models do not exceed 2.07%, with a minimum correlation coefficient of 0.99. In addition, the dimensionality of inputs was reduced from four variables to the two most cost-effective variables in DNI prediction. Therefore, these suggested models are reliable for estimating DNI in the arid desert areas of Algeria and other locations with similar climatic features. (C) 2022 The Authors. Published by Elsevier Ltd.
英文关键词	Solar energy Direct normal irradiation Concentrating solar power operation Algerian big south Extremal optimization Long Short-Term Memory
类型	Article
语种	英语
开放获取类型	gold, Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000892862100002
WOS关键词	PERFORMANCE ANALYSIS ; AIR-TEMPERATURE ; RADIATION ; ENERGY ; OPTIMIZATION ; METHODOLOGY ; GENERATION
WOS类目	Energy & Fuels
WOS研究方向	Energy & Fuels
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392427
推荐引用方式 GB/T 7714	Djaafari, Abdallah,Ibrahim, Abdelhameed,Bailek, Nadjem,et al. Hourly predictions of direct normal irradiation using an innovative hybrid LSTM model for concentrating solar power projects in hyper-arid regions[J],2022,8:15548-15562.
APA	Djaafari, Abdallah.,Ibrahim, Abdelhameed.,Bailek, Nadjem.,Bouchouicha, Kada.,Hassan, Muhammed A..,...&El-Kenawy, El-Sayed M..(2022).Hourly predictions of direct normal irradiation using an innovative hybrid LSTM model for concentrating solar power projects in hyper-arid regions.ENERGY REPORTS,8,15548-15562.
MLA	Djaafari, Abdallah,et al."Hourly predictions of direct normal irradiation using an innovative hybrid LSTM model for concentrating solar power projects in hyper-arid regions".ENERGY REPORTS 8(2022):15548-15562.