Prediction of Extreme Wind Speed Using Artificial Neural Network Approach

Prediction of an accurate wind speed of wind farms is necessary because of the intermittent nature of wind for any region. Number of methods such as persistence, physical, statistical, spatial correlation, artificial intelligence network and hybrid are generally available for prediction of wind speed. In this paper, ANN based methods viz., Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are used. The performance of the networks applied for prediction of wind speed is evaluated by model performance indicators viz., Correlation Coefficient (CC), Model Efficiency (MEF) and Mean Absolute Percentage Error (MAPE). Meteorological parameters such as maximum and minimum temperature, air pressure, solar radiation and altitude are considered as input units for MLP and RBF networks to predict the extreme wind speed at Delhi. The study shows the values of CC, MEF and MAPE between the observed and predicted wind speed (using MLP) are computed as 0.992, 95.4% and 4.3% respectively while training the network data. For RBF network, the values of CC, MEF and MAPE are computed as 0.992, 95.9% and 3.0% respectively. The model performance analysis indicates the RBF is better suited network among two different networks studied for prediction of extreme wind speed at Delhi.