Employing Data Analytics and Machine Learning for Analyzing and Forecasting Wind Speed Data

Big data analytics is being used for various applications presently, out of which statistical analysis for renewable energy is of great importance. Out of all present renewable sources of energy, wind energy is the one which is most uncertain in nature. This is because wind speed changes continuously with time leading to uncertainty in availability of amount of wind power generated. Hence, a short-term forecasting of wind speed will help in prior estimation of wind power generation availability and well as future long term plans. This study presents a comparative study of a Wind speed forecasting model using Artificial Neural Networks (ANN) with back propagation. Here an attempt is made to forecast Wind Speed using ANN with back propagation method and their results are compared based on their convergence speed in training period and their performance in testing period on the basis of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE).