Wind Energy Forecasting Using Radial Basis Function Neural Networks

Wind power forecast is essential for a wind farm developer for comprehensive assessment of wind potential at a particular site or topographical location. Wind energy potential at any given location is a non –linear function of mean average wind speed, vertical wind profile, energy pattern factor, peak wind speed, prevailing wind direction, lull hours, air density and a few other parameters. Wind energy pattern data of various locationsis collected from a published resource data book by Centre for Wind Energy Technology, India.Modeling of wind energy forecasting problem consists of data collection, input-output selection, mappingand simulation. In this work, artificial neural networks technique is adopted to deal with the wind energy forecasting problem.After normalization, neural network will be run with training dataset.Radial Basis function based Neural Networks is a feed-forward algorithm of artificial neural networks that offers supervised learning.It establishes local mapping with two fold learning quickly.Wind power densities predicted for new locationsare in agreement with the measured values atthewind monitoring stations.MAPE was found out to be less than 10% for all the values of Wind Power Density predictions at new topographical locations and R is found to be nearer to unity.WPD values are multiplied by wind availability hours (generation hours) in that particular location to give number of energy units at the turbine output. These values are compared to the output of the wind turbine model installed in the same region, so as to assess the number of units generated by that particular wind turbine in the respective locations.This kind of assessment is useful for wind energy projects during feasibility studies. With this work, it is established that radial basis function neural netscan be used as a diagnostic tool for function approximation problemsconnected towind energy resourcemodeling& forecast.