A Comparative Study of Activation Functions of NAR and NARX Neural Network for Long-Term Wind Speed Forecasting in Malaysia
Document Type
Article
Publication Date
1-1-2019
Abstract
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.
Keywords
Activation functions, Artificial neural network modeling, Comparative studies, Meteorological station, NARX neural network, Neural network (nn), Time series forecasting, Wind speed forecasting
Divisions
fac_eng
Funders
Ministry of Higher Education of Malaysia and University Malaya (ERGS nos. ER0142013A, RP015C-13AET),High Impact Research Grant (HIR-D000006-16001)
Publication Title
Mathematical Problems in Engineering
Volume
2019
Publisher
Hindawi