Global solar radiation prediction over North Dakota using air temperature: Development of novel hybrid intelligence model

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Global solar radiation prediction over North Dakota using air temperature : Development of novel hybrid intelligence model

Tao, Hai (författare): School of Computer Science, Baoji University of Arts and Sciences, 721007, China

Ewees, Ahmed A. (författare): Department of e-Systems, University of Bisha, Bisha 61922, Saudi Arabia. Department of Computer, Damietta University, Damietta 34517, Egypt

Al-Sulttani, Omran (författare): Department of Water Resources Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq

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Beyaztas, Ufuk (författare): Department of Economics and Finance, Piri Reis University, Istanbul, Turkey

Hameed, Mohammed Majeed (författare): Department of Civil Engineering, Al-Maaref University College, Ramadi, Iraq

Salih, Sinan Q. (författare): Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam. Computer Science Department, Dijlah University College, Baghdad, Iraq

Armanuos, Asaad M. (författare): Irrigation and Hydraulics Engineering Department, Civil Engineering Department, Faculty of Engineering, Tanta University, Egypt

Al-Ansari, Nadhir, 1947- (författare): Luleå tekniska universitet,Geoteknologi

Voyant, Cyril (författare): University of Corsica, CNRS UMR SPE 6134, 20250 Corte, France

Shahid, Shamsuddin (författare): School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310, Johor Bahru, Malaysia

Yaseen, Zaher Mundher (författare): Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam

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School of Computer Science, Baoji University of Arts and Sciences, 721007, China Department of e-Systems, University of Bisha, Bisha 61922, Saudi Arabia Department of Computer, Damietta University, Damietta 34517, Egypt (creator_code:org_t)

Netherland : Elsevier, 2021
2021
Engelska.
Ingår i: Energy Reports. - Netherland : Elsevier. - 2352-4847. ; 7, s. 136-157

Relaterad länk:: https://doi.org/10.1...; visa fler...; https://ltu.diva-por... (primary) (Raw object); https://doi.org/10.1...; https://urn.kb.se/re...; https://doi.org/10.1...; visa färre...

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Accurate solar radiation (SR) prediction is one of the essential prerequisites of harvesting solar energy. The current study proposed a novel intelligence model through hybridization of Adaptive Neuro-Fuzzy Inference System (ANFIS) with two metaheuristic optimization algorithms, Salp Swarm Algorithm (SSA) and Grasshopper Optimization Algorithm (GOA) (ANFIS-muSG) for global SR prediction at different locations of North Dakota, USA. The performance of the proposed ANFIS-muSG model was compared with classical ANFIS, ANFIS-GOA, ANFIS-SSA, ANFIS-Grey Wolf Optimizer (ANFIS-GWO), ANFIS-Particle Swarm Optimization (ANFIS-PSO), ANFIS-Genetic Algorithm (ANFIS-GA) and ANFISDragonfly Algorithm (ANFIS-DA). Consistent maximum, mean and minimum air temperature data for nine years (2010–2018) were used to build the models. ANFIS-muSG showed 25.7%–54.8% higher performance accuracy in terms of root mean square error compared to other models at different locations of the study areas. The model developed in this study can be employed for SR prediction from temperature only. The results indicate the potential of hybridization of ANFIS with the metaheuristic optimization algorithms for improvement of prediction ccuracy.

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Av författaren/redakt...: Tao, Hai; Ewees, Ahmed A.; Al-Sulttani, Omr ...; Beyaztas, Ufuk; Hameed, Mohammed ...; Salih, Sinan Q.; visa fler...; Armanuos, Asaad ...; Al-Ansari, Nadhi ...; Voyant, Cyril; Shahid, Shamsudd ...; Yaseen, Zaher Mu ...; visa färre...

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Global solar radiation prediction over North Dakota using air temperature : Development of novel hybrid intelligence model

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