Wind Power Forecasting Using Wavelet Transform and General Regression Neural Network for Ontario Electricity Market

(E-pub Ahead of Print)

Author(s): Sumit Saroha*, S. K. Aggarwal.

Journal Name: Recent Advances in Electrical & Electronic Engineering
Formerly Recent Patents on Electrical & Electronic Engineering

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Abstract:

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, this article presents Wavelet Transform (WT) based General Regression Neural Network (GRNN) with statistical time series input selection technique.

Result: 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.

Keywords: General Regression Neural Network, Time Series, Wavelet Transform, Wind Power Forecasting

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Article Details

(E-pub Ahead of Print)
DOI: 10.2174/2352096512666190118160604
Price: $95