Bayesian Optimization Based ANN Model for Short Term Wind Speed Forecasting in Newfoundland, Canada

Abstract

Wind power capacity around the world is increasing day by day, but the production of wind energy greatly depends on the wind speed, where the wind speed has stochastic nature over time. In this paper, an artificial neural network (ANN) technique to forecast wind speed for the next hour in Newfoundland, Canada is proposed. As, deep learning models are combined with different hyperparameters, in our study, the selection of important hyperparameters are conducted by applying the Bayesian optimization algorithm. The wind speed forecasting performance of the proposed model is compared with other recognized models like support vector machine (SVM), random forest (R.F.) and decision tree (D.T.), where it is observed that our proposed model performs better than the other models in terms of mean absolute error (M.A.E.) and root mean squared error (R.M.S.E.). The proposed Bayesian optimized artificial neural network is fed with five input features and delivers M.A.E. and R.M.S.E. of 1.09 and 1.45.