Wind speed estimation using neural networks

Abstract

In electrical power system, prediction of Renewable energy sources has become essential for designing a control strategy to manage the electricity on the grid. To help the system operators for integration of wind power system to the existing power system, wind speed and power prediction is essential.Basically neural network is aimed for short-term forecasting problems as it is capable to learn non-linear relationship between inputs and outputs by a non-statistical approach and don’t require any predefined mathematical model. This thesis investigates the effectiveness of recurrent wavelet neural network (RWNN) and artificial wavelet neural network (AWNN) dynamics for wind speed forecasting. We evaluate the RWNN and AWNN against multilayer feed-forward neural network. The RWNN and AWNN are trained using back propagation gradient descent algorithm. The experimental results show that the performance of RWNN and AWNN approaches outperforms the multilayer feed-forward neural network. All the three models use Hourly averaged time series data (2982 numbers of samples) for wind speed collected from the National Renewable Energy Laboratory (NREL).