Multi step ahead forecasting of wind power by genetic algorithm based neural networks

Abstract

In present day scenario statistical (time series) and physical (NWP) models are utilized for wind power forecasting and many of them are using neural networks to obtain greater accuracy of wind power prediction at final stage. In a time series framework, forecasting is categorised into two ways single step ahead and multi-step ahead. In this paper an advanced time-series model for multi-step ahead wind power prediction based on artificial intelligence techniques is presented. This method requires an input of past measurements for prediction & input is settled on the basis of statistical tool called Auto Correlation Function (ACF). Genetic Algorithms based Neural Network (GANN) and Feed Forward Neural Network (FFNN) trained by Levenberg-Marquardt (LM) training algorithm are employed. Mean absolute error (MAE) and mean absolute percentage error (MAPE) are considered as the performance metric and both models are also compared with persistence model. The data of wind power has been collected from Ontario Electricity Market for the year 2009–12 and tested for one year up to 12 multi-steps ahead forecasting. It has been observed that GANN gives better performance as compared to FFNN.