Wind Power Prediction Using Neural Networks with Different Training Models

Abstract

Energy in any form is a vital source of producing electricity for daily utilization. Wind energy source as renewable energy is playing a pivotal role in generating power from electric gird owing to environmentally friendly feature. Due to the volatile and intermittent nature of wind energy, fluctuations and disparities occur in installing, monitoring, and planning in an energy management system. Therefore, forecasting and prediction are promising solutions to address mismanagement at the grid. Consequently, machine learning tools specifically neural networks have created a huge impact in forecasting wind power. In this study, the feed-forward neural network is adopted for predicting wind power. Additionally, for having precise and efficient results, different training models i.e. one-step sacent, resilient propagation, Bayesian regularization, scaled-conjugate gradient back propagations, and Levenberg-Marquardt are used to make the comparative analysis. From the simulations and results, it was concluded that Bayesian regularization training model is performing best and achieving high accuracy by obtaining 1.66 of RMSE and 6.06 of %MAPE. Eventually, it is concluded that neural networks can be a good choice to predict wind power for optimal solutions. Moreover, the proposed model can be applied to other renewable energy source predictions.