Long-term modelling of wind speeds using six different heuristic artificial intelligence approaches
Article
Article Title | Long-term modelling of wind speeds using six different heuristic artificial intelligence approaches |
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ERA Journal ID | 1969 |
Article Category | Article |
Authors | Maroufpoor, Saman (Author), Sanikhani, Hadi (Author), Kisi, Ozgur (Author), Deo, Ravinesh C. (Author) and Yaseen, Zaher Mundher (Author) |
Journal Title | International Journal of Climatology |
Journal Citation | 39 (8), pp. 3543-3557 |
Number of Pages | 15 |
Year | 2019 |
Publisher | John Wiley & Sons |
Place of Publication | United Kingdom |
ISSN | 0899-8418 |
1097-0088 | |
Digital Object Identifier (DOI) | https://doi.org/10.1002/joc.6037 |
Web Address (URL) | https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.6037 |
Abstract | Wind speed is an essential component that needs to be determined accurately, especially over long‐term periods for various engineering and scientific purposes including renewable energy productions, structural building sustainability and others. In this study, six different heuristic methods: multi‐layer perceptron artificial neural networks, (ANN), adaptive neuro‐fuzzy inference system (ANFIS) with grid partition (GP), ANFIS with subtractive clustering (SC), generalized regression neural networks (GRNN), gene expression programming (GEP) and multivariate adaptive regression spline (MARS) are developed to model monthly wind speeds using meteorological input information. The atmospheric pressure, temperature, relative humidity and rainfall values are obtained from Jolfa and Tabriz meteorological stations, Iran, and are used to build the proposed predictive models.. Different statistical indicators are computed to evaluate and comprehensively assess the performance of the six heuristic methods. Over the testing phase, the ANFIS‐GP and GRNN models are seen to exhibit the highest predictive performance for the Jolfa and Tabriz stations, respectively. That is, the maximum coefficient of determination are found to be 0.874, 0.858, 0.850, 0.849, 0.847 and 0.826, for the GRNN, ANFIS‐GP, ANFIS‐SC, ANN, GEP and MARS models, respectively, for Jolfa station, respectively, revealing the superiority of GRNN over the five counterpart models. The results show the generalization capability of the tested heuristic artificial intelligence techniques for both study stations, and therefore could be explored for windspeed prediction and various decisions made in regards to climate change studies. |
Keywords | gene expression programming, multivariate adaptive regression spline, neural networks, neuro-fuzzy, prediction, wind speed |
ANZSRC Field of Research 2020 | 460510. Recommender systems |
490399. Numerical and computational mathematics not elsewhere classified | |
410404. Environmental management | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Tehran, Iran |
University of Kurdistan, Iran | |
Ilia State University, United States | |
School of Agricultural, Computational and Environmental Sciences | |
Ton Duc Thang University, Vietnam | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5396/long-term-modelling-of-wind-speeds-using-six-different-heuristic-artificial-intelligence-approaches
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