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

• Published in: Mathematical problems in engineering Vol. 2019; no. 2019; pp. 1 - 14
• Main Authors: Chong, W. T., Hossain, Sazzad, Julai, Sabariah, Sarkar, Rasel, Rahman, Mahmudur
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2019; Hindawi; Hindawi Limited
• Subjects: Activation; Alternative energy sources; Artificial intelligence; Artificial neural networks; Comparative studies; Electric power generation; Electricity distribution; Electricity generation; Energy management; Engineering; Forecasting; Fossil fuels; Neural networks; Renewable resources; Short term; Time series; Transfer functions; Turbines; Wavelet transforms; Wind power; Wind power generation; Wind speed; Malaysia
• ISSN: 1024-123X; 1563-5147
• DOI: 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.