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 R

2

statistic. 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.