Arid
DOI10.32604/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
ISSN1546-2218
EISSN1546-2226
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.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[El-Kenawy, El-Sayed M.]的文章
[Ibrahim, Abdelhameed]的文章
[Bailek, Nadjem]的文章
百度学术
百度学术中相似的文章
[El-Kenawy, El-Sayed M.]的文章
[Ibrahim, Abdelhameed]的文章
[Bailek, Nadjem]的文章
必应学术
必应学术中相似的文章
[El-Kenawy, El-Sayed M.]的文章
[Ibrahim, Abdelhameed]的文章
[Bailek, Nadjem]的文章
相关权益政策
暂无数据
收藏/分享

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。