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Improving generalisation capability of artificial intelligence-based solar radiation estimator models using a bio-inspired optimisation algorithm and multi-model approach

Moazenzadeh, Roozbeh ; Mohammadi, Babak ; Duan, Zheng ; Delghandi, Mahdi

Environmental science and pollution research international, 2022-04, Vol.29 (19), p.27719-27737 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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Título:
Improving generalisation capability of artificial intelligence-based solar radiation estimator models using a bio-inspired optimisation algorithm and multi-model approach
Autor: Moazenzadeh, Roozbeh ; Mohammadi, Babak ; Duan, Zheng ; Delghandi, Mahdi
Assuntos: Algorithms ; Aquatic Pollution ; Artificial Intelligence ; Atmospheric models ; Atmospheric Protection/Air Quality Control/Air Pollution ; Biomimetics ; Clean energy ; Climate change ; Climate effects ; Climate Research ; Earth and Environmental Science ; Earth and Related Environmental Sciences ; Ecotoxicology ; Empirical analysis ; Environment ; Environmental Chemistry ; Environmental Health ; Fossil fuels ; Geovetenskap och miljövetenskap ; Hydroinformatics ; hydrological modeling ; Klimatforskning ; Mathematical models ; Meteorologi och atmosfärforskning ; Meteorological parameters ; Meteorology ; Meteorology and Atmospheric Sciences ; Natural Sciences ; Naturvetenskap ; Optimization ; Parameter estimation ; Pollution control ; Radiation ; Radiation measurement ; Remote sensing ; Renewable energy ; Research Article ; Search algorithms ; Solar Energy ; Solar radiation ; Support Vector Machine ; Support vector machines ; Training ; Waste Water Technology ; Water Management ; Water Pollution Control ; Weather stations
É parte de: Environmental science and pollution research international, 2022-04, Vol.29 (19), p.27719-27737
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
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Descrição: One way of reducing environmental pollution is to reduce our dependence on fossil fuels by replacing them with solar radiation (Rs), which is one of the main sources of clean and renewable energy. In this study, daily Rs values at seven meteorological stations in Iran (Ahvaz, Isfahan, Kermanshah, Mashhad, Bandar Abbas, Kerman and Tabriz) over 2010–2019 were estimated using empirical models, support vector machine (SVM), SVM coupled with cuckoo search algorithm (SVM-CSA) and multi-model approach in the form of two structures. In structure 1, data from each station were divided into training and testing sets. In structure 2, data from the former four stations were used for model training, and those from the latter three stations were used to test the models. The results showed that using meteorological parameters improved estimation accuracy compared with the use of geographical parameters for both SVM and SVM-CSA models. Coupling the CSA to SVM did improve the accuracy of radiation estimates, reducing RMSE by up to 38% (Kermanshah station) and 36% (Tabriz station) for the first structure and about 42.4% (Tabriz station) for the second. Performance analysis of the models over three intervals including, the first, middle and last third of measured radiation values at each station showed that for both structures (except at Tabriz station), the best model performance in under- and over-estimation sets of radiation values was obtained, respectively, in the first third interval (first structure, Mashhad station, RMSE = 28.39 J.cm −2 .day −1 ) and the last third interval (first structure, Bandar Abbas station, RMSE = 12.23 J.cm −2 .day −1 ). Determining the effects of climate change on Rs estimation and using remotely sensed data as inputs of the models could be considered as future works.
Editor: Berlin/Heidelberg: Springer Berlin Heidelberg
Idioma: Inglês

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Improving generalisation capability of artificial intelligence-based solar radiation estimator models using a bio-inspired optimisation algorithm and multi-model approach

Moazenzadeh, Roozbeh ; Mohammadi, Babak ; Duan, Zheng ; Delghandi, Mahdi

Berlin/Heidelberg: Springer Berlin Heidelberg

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