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.
Highlights
Is the world’s total consumption of electricity rapidly increasing, and the greenhouse gas (GHG) emission is increasing by the power generation from fossil fuels
The main objective of this study is to compare the performance of activation functions of Nonlinear Autoregressive (NAR) and Nonlinear Autoregressive Exogenous (NARX) neural network for 1-month-ahead wind speed forecasting (WSF) for three different regions in Malaysia
The first three months of wind speed data have been used for training and last one-month data have been used for testing in Nonlinear Autoregressive Neural Network (NARNN) and Nonlinear Autoregressive Exogenous Neural Network (NARXNN), respectively
Summary
Is the world’s total consumption of electricity rapidly increasing, and the greenhouse gas (GHG) emission is increasing by the power generation from fossil fuels. AI, namely, NAR and NARX, neural network has been chosen for wind speed forecasting due to higher forecasting accuracy and no mathematical model required. The most effective way of long-term WSF has been found to be AI methods since they do not require mathematical model other than their own universal algorithm for future time series prediction. This is why the contribution of this study is that it examined the performance of two activation functions: hyperbolic tangent sigmoid (tansig) and logistic sigmoid (logsig) when used in different time series networks such as NAR and NARX with different time series datasets but with the same network parameters and architectures Such analysis will reveal if any of the activation functions consistently perform better than the other in different conditions, so that future researchers choose the proper activation functions while conducting neural network-based time series forecasting tasks.
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