Wind power forecasting using wavelet transforms and neural networks with tapped delay

Abstract

With an objective to improve wind power estimation accuracy and reliability, this paper presents Linear Neural Networks with Tapped Delay (LNNTD) in combination with wavelet transform (WT) for probabilistic wind power forecasting in a time series framework. For comparison purposes, results of the proposed model are compared with the benchmark model, different neural networks and WT based models considering performance indices such as accuracy, execution time and R2statistic. For the reliability and proper validation of the proposed model, this paper highlights the probabilistic forecast attributes at different skill tests. The historical data of the Ontario Electricity Market (OEM) for the period 2011-2014 were used and tested for two years from November 2012 to October 2014 with one month moving window considering all seasonal aspects. The experimental results clearly show that the results of the proposed model have been found to be better than others.