Wind is one of the prominent renewable source, as it is clean energy and abundantly available, however there are immense issues owing to varying nature of wind flow. The said issues has attracted many researchers to work pertinent to wind forecasting models and hence it is possible for wind power forecast based on the capacity of wind turbine based energy conversion system. With the devised forecasting techniques, it is also possible to schedule required demand to match the generation. Forecasting is an important aid in wind speed prediction. The wind speed forecasting can effectively reduce or avoid the adverse effect of wind farm on power grid and it will also ensure the safe and economic operations of the power system. This work is aimed to design an appropriate model for wind forecasting with less mean absolute error (MAE), mean absolute percentage error (MAPE) and mean square error (MSE) in comparison to back propagation neural network (BPNN) and linear regression technique. The simulation of the mentioned and proposed models are done and the results are presented to validate the effectiveness of the proposed technique
cited By 0; Conference of 2nd Biennial International Conference on Power and Energy Systems, PESTSE 2016 ; Conference Date: 21 January 2016 Through 23 January 2016; Conference Code:122805
K. Kiranvishnu, Dr. J. Ramprabhakar, and K. Sireesha, “Comparative study of wind speed forecasting techniques”, in 2016 - Biennial International Conference on Power and Energy Systems: Towards Sustainable Energy, PESTSE 2016, 2016.