Short-term Wind Speed Forecasting of Coastal Line of Peninsular India Using NARX Models

Being over reliant on fossils fuels has resulted in massive increase of pollution levels causing the average global temperature to rise. Keeping in mind that, power extraction from renewable energy sources have been of great interest for all nations. Extracting energy from wind is a popular and sustainable source of energy. Since wind speeds are intermittent in nature, prediction of wind speeds is an important aspect in power generation through wind turbines. This work focuses on wind speed prediction along the coastal line of peninsular India taking four time-related parameters and eight meteorological parameters wherein past wind speeds are also used as an input of twenty-seven sites. The data has been collected from Indian Meteorological Department for a span of five years (2016 - 2020), three-hour average. Time-series prediction has been done using Levenberg-Marquardt (LM), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms in MATLAB and a comparative study has been done while altering the training, validation and testing percentages along with number of hidden layers in the neural network to identify the best algorithm with the help of linear regression and mean square error. Further sensitivity analysis is done amongst all the seven meteorological parameters in order to identify the most and least wind speed affecting factors.