Comparative study of Wind Speed Forecasting using Machine Learning

With the usage of De-regulated power system lately, power system maintenance has been a major issue. In order to maintain microgrid stability and reliability, wind speed prediction is one of the important factors and necessary as well. Because of the negative ecological effect of utilizing fossil fuel sources and exhaustion of petroleum products, the elective fuel sources are being looked through everywhere on the world. Since wind energy is perfect and sustainable, the entrance of wind energy for power generation is expanding step by step. Prediction of wind speed is also used in computing the wind energy. Wind speed is non-linear in nature and hence prediction of wind speed is foremost. The ability of Machine Learning techniques to work with inadequate commands makes it superior over other techniques. So, the proposed model uses Back Propagation Network (BPN), Radial Basis Neural network (RBNN), Gradient Boost Regression method (GBR) to conclude the network which gives a better output to predict the wind speed.