Objective:
The estimation accuracy of wind power is an important subject of concern for
reliable grid operations and taking part in open access. So, with an objective to improve the wind
power forecasting accuracy.
Methods:
This article presents Wavelet Transform (WT) based General Regression Neural Network
(GRNN) with statistical time series input selection technique.
Results:
The results of the proposed model are compared with four different models namely naïve
benchmark model, feed forward neural networks, recurrent neural networks and GRNN on the basis
of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) performance metric.
Conclusion:
The historical data used by the presented models has been collected from the Ontario
Electricity Market for the year 2011 to 2015 and tested for a long time period of more than two years
(28 months) from November 2012 to February 2015 with one month estimation moving window.
The State-Of-The-Art on Wind Climate Assessment and Wind Power Forecasting
