Solar radiation estimation in different climates with meteorological variables using Bayesian model averaging and new soft computing models

Energy Reports

Changhyun Jun¹  Huan-Ming Chuang¹  Guodao Zhang²  Hamza Turabieh³  Sayed M. Bateni⁴  Massoud Moslehpour⁵  Amir Mosavi⁶  Majdi Mafarja⁷  Shahab S. Band⁸ 

[1] Corresponding authors.;College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China;Department of Civil and Environmental Engineering and Water Resources Research Center, the University of Hawaii at Manoa, Honolulu, HI, 96822, USA;Department of Civil and Environmental Engineering, College of Engineering, Chung-Ang University, Seoul 06974, Republic of Korea;Department of Computer Science, Birzeit University, Birzeit 72439, Palestine;Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, 21944, Taif, Saudi Arabia;Department of information management, National Yunlin University of Science and Technology, Douliu, Yunlin, Taiwan;Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Yunlin 64002, Taiwan;

关键词: Solar radiation;    Energy management;    Soft computing models;    Archimedes optimization algorithm;   

DOI  :  

来源: DOAJ

【 摘 要 】

Solar radiation (SR) is considered as a critical factor in determining energy management. In this research, the potential of the Bayesian averaging model (BMA) was investigated for estimating monthly SR. The inputs were monthly average temperature, wind speed, relative humidity, and sunshine duration. The BMA model was employed to estimate SR by extracting information from multiple adaptive neuro-fuzzy systems (ANFIS) and multi-layer perceptron (MLP) models. In this study, Archimedes optimization algorithm (AOA), particle swarm optimization (PSO), genetic algorithm (GA), and bat algorithm (BA) were used to tune the parameters of the ANIFS and MLP. In addition, a multitude of error indices such as root mean square error (RMSE), and Nash Sutcliff efficiency (NSE), and several graphical tools were used to investigate the accuracy of the models. The results showed the better performance of the BMA model than other models for estimating solar radiation. For example, BMA with RMSE of 6.78, MAE of 5.25, and NSE of 0.96 had the best accuracy in the training stage of the Tabriz station. On the other hand, in the testing level of Tehran station, BMA (RMSE=7.89 MJ/ m2, MAE=6.89 MJ/ m2, NSE=0.95) gave the best accuracy, and the MLP model (RMSE= 14.12 MJ/ m2, MAE=12.23 MJ/ m2, and NSE=0.77) gave the worst performance, respectively.

【 授权许可】

Unknown