Long‐term modelling of wind speeds using six different heuristic artificial intelligence approaches

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.

Reliability-based design and implementation of crow search algorithm for longitudinal dispersion coefficient estimation in rivers

“…On the other hand, AI techniques have been recruited to overcome the disadvantage of regression-based methods in predicting various problems. In particular, machine learning methods, e.g., artificial neural networks (ANNs), support vector machine (SVM), model tree (MT), as well as metaheuristic algorithms, have recently shown promising results (Li et al 2016 ; Wang et al 2016 ; Zounemat-Kermani et al 2016 ; Rezaie-Balf et al 2017 ; Deo et al 2018 ; Horton et al 2018 ; Kisi et al 2019 ; Najafzadeh and Ghaemi 2019 ; Fallah et al 2019 ; Ghaemi et al 2019 ; Maroufpoor et al 2019 ; Bozorg-Haddad et al 2019 ). In the case of LDC, although many studies (e.g., Adarsh 2010 ; Etemad-Shahidi and Taghipour 2012 ; Li et al 2013 ; Najafzadeh and Tafarojnoruz 2016 ; Alizadeh et al 2017b ; Noori et al 2017 ; Seifi and Riahi-Madvar 2019 ; Riahi-Madvar et al 2019 ) have been performed during the last decades in order to predict this complicated phenomenon with high precision, the estimation results have not been adequately accurate or reliable.…”

The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling

“…Wind energy is a clean and renewable source, which can be dependent on for the very long-term future [1,2]. The use of wind energy saves fossil fuels, since it is non-polluting in nature and its generated energy does not lead to greenhouse gases and radioactivity.…”

Modelling daily soil temperature at different depths via the classical and hybrid models

“…Over the last years, the AI-based techniques have been proposed in various areas of research, such as meteorological, hydrological and soil sciences (Tabari et al, 2011;Talaee, 2014;Aitkenhead and Coull, 2016;Liu et al, 2016;Citakoglu, 2017;Mehdizadeh et al, 2017aMehdizadeh et al, , 2018aGavili et al, 2018;Massawe et al, 2018;Singh et al, 2018;Maroufpoor et al, 2019;Azad et al, 2020;Mehdizadeh, 2020). For example, the AI approaches were applied successfully by Mehdizadeh (2018a), Mehdizadeh et al (2017b) and Azad et al (2020) to estimate the dew point and air temperatures.…”

“…For example, the AI approaches were applied successfully by Mehdizadeh (2018a), Mehdizadeh et al (2017b) and Azad et al (2020) to estimate the dew point and air temperatures. In another works, the potential of AI models was verified to estimate the wind speed and evapotranspiration time series (Gavili et al, 2018;Maroufpoor et al, 2019;Mohammadi and Mehdizadeh, 2020).…”

“…(2020) to estimate the dew point and air temperatures. In another works, the potential of AI models was verified to estimate the wind speed and evapotranspiration time series (Gavili et al ., 2018; Maroufpoor et al ., 2019; Mohammadi and Mehdizadeh, 2020).…”