干旱区生态环境知识资源中心(Arid): Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria

Arid

DOI	10.32604/cmc.2022.023257
	Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria
	El-Kenawy, El-Sayed M.; Ibrahim, Abdelhameed; Bailek, Nadjem; Bouchouicha, Kada; Hassan, Muhammed A.; Jamil, Basharat; Al-Ansari, Nadhir
通讯作者	Bailek, N
来源期刊	CMC-COMPUTERS MATERIALS & CONTINUA
ISSN	1546-2218
EISSN	1546-2226
出版年	2022
卷号	71 期号:3 页码:5837-5854
英文摘要	In order to achieve a highly accurate estimation of solar energy resource potential, a novel hybrid ensemble-learning approach, hybridizing Advanced Squirrel-Search Optimization Algorithm (ASSOA) and support vector regression, is utilized to estimate the hourly tilted solar irradiation for selected arid regions in Algeria. Long-term measured meteorological data, including mean-air temperature, relative humidity, wind speed, alongside global horizontal irradiation and extra-terrestrial horizontal irradiance, were obtained for the two cities of Tamanrasset-and-Adrar for two years. Five computational algorithms were considered and analyzed for the suitability of estimation. Further two new algorithms, namely Average Ensemble and Ensemble using support vector regression were developed using the hybridization approach. The accuracy of the developed models was analyzed in terms of five statistical error metrics, as well as theWilcoxon rank-sum and ANOVA test. Among the previously selected algorithms, K Neighbors Regressor and support vector regression exhibited good performances. However, the newly proposed ensemble algorithms exhibited even better performance. The proposed model showed relative root mean square errors lower than 1.448% and correlation coefficients higher than 0.999. This was further verified by benchmarking the new ensemble against several popular swarm intelligence algorithms. It is concluded that the proposed algorithms are far superior to the commonly adopted ones.
英文关键词	Renewable energy resources hybrid modeling tilted solar irradiation arid region
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000770817300081
WOS关键词	GLOBAL SOLAR IRRADIATION ; ARTIFICIAL NEURAL-NETWORK ; INCLINED SURFACES ; TILTED PLANES ; RADIATION ; PREDICTION ; MODELS
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/2XILL650/392158
推荐引用方式 GB/T 7714	El-Kenawy, El-Sayed M.,Ibrahim, Abdelhameed,Bailek, Nadjem,et al. Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria[J],2022,71(3):5837-5854.
APA	El-Kenawy, El-Sayed M..,Ibrahim, Abdelhameed.,Bailek, Nadjem.,Bouchouicha, Kada.,Hassan, Muhammed A..,...&Al-Ansari, Nadhir.(2022).Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria.CMC-COMPUTERS MATERIALS & CONTINUA,71(3),5837-5854.
MLA	El-Kenawy, El-Sayed M.,et al."Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria".CMC-COMPUTERS MATERIALS & CONTINUA 71.3(2022):5837-5854.