Using optimal choice of parameters for meta-extreme learning machine method in wind energy application
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CitationDokur, E., Karakuzu, C., Yüzgeç, U., & Kurban, M. (2021). Using optimal choice of parameters for meta-extreme learning machine method in wind energy application. COMPEL-The international journal for computation and mathematics in electrical and electronic engineering.
Purpose – This paper aims to deal with the optimal choice of a novel extreme learning machine (ELM) architecture based on an ensemble of classic ELM called Meta-ELM structural parameters by using a forecasting process. Design/methodology/approach – The modelling performance of the Meta-ELM architecture varies depending on the network parameters it contains. The choice of Meta-ELM parameters is important for the accuracy of the models. For this reason, the optimal choice of Meta-ELM parameters is investigated on the problem of wind speed forecasting in this paper. The hourly wind-speed data obtained from Bilecik and Bozcaada stations in Turkey are used. The different number of ELM groups (M) and nodes (Nh) are analysed for determining the best modelling performance of Meta-ELM. Also, the optimal Meta-ELM architecture forecasting results are compared with four different learning algorithms and a hybrid meta-heuristic approach. Finally, the linear model based on correlation between the parameters was given as three dimensions (3D) and calculated. Findings – It is observed that the analysis has better performance for parameters of Meta-ELM, M = 15-20 and Nh = 5-10. Also considering the performance metric, the Meta-ELM model provides the best results in all regions and the Levenberg–Marquardt algorithm -feed-forward neural network and adaptive neuro-fuzzy inference system -particle swarm optimization show competitive results for forecasting process. In addition, the Meta-ELM provides much better results in terms of elapsed time. Originality/value – The original contribution of the study is to investigate of determination Meta-ELM parameters based on forecasting process.