A Comparative Study of Activation Functions of NAR and NARX Neural Network for Long-Term Wind Speed Forecasting in Malaysia

Sarkar, Rasel and Julai, Sabariah and Hossain, Sazzad and Chong, Wen Tong and Rahman, Mahmudur (2019) A Comparative Study of Activation Functions of NAR and NARX Neural Network for Long-Term Wind Speed Forecasting in Malaysia. Mathematical Problems in Engineering, 2019. pp. 1-14. ISSN 1024-123X, DOI https://doi.org/10.1155/2019/6403081.

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Official URL: https://doi.org/10.1155/2019/6403081


Since wind power is directly influenced by wind speed, long-term wind speed forecasting (WSF) plays an important role for wind farm installation. WSF is essential for controlling, energy management and scheduled wind power generation in wind farm. The proposed investigation in this paper provides 30-days-ahead WSF. Nonlinear Autoregressive (NAR) and Nonlinear Autoregressive Exogenous (NARX) Neural Network (NN) with different network settings have been used to facilitate the wind power generation. The essence of this study is that it compares the effect of activation functions (namely, tansig and logsig) in the performance of time series forecasting since activation function is the core element of any artificial neural network model. A set of wind speed data was collected from different meteorological stations in Malaysia, situated in Kuala Lumpur, Kuantan, and Melaka. The proposed activation functions tansig of NARNN and NARXNN resulted in promising outcomes in terms of very small error between actual and predicted wind speed as well as the comparison for the logsig transfer function results. © 2019 Rasel Sarkar et al.

Item Type: Article
Funders: Ministry of Higher Education of Malaysia and University Malaya (ERGS nos. ER0142013A, RP015C-13AET), High Impact Research Grant (HIR-D000006-16001)
Uncontrolled Keywords: Activation functions; Artificial neural network modeling; Comparative studies; Meteorological station; NARX neural network; Neural network (nn); Time series forecasting; Wind speed forecasting
Subjects: T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 22 Jan 2020 01:38
Last Modified: 22 Jan 2020 01:38
URI: http://eprints.um.edu.my/id/eprint/23521

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