Knowledge Resource Center for Ecological Environment in Arid Area
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. |
条目包含的文件 | 条目无相关文件。 |
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。