Abstract
Due to the depletion of fossil fuel and global warming, the incorporation of alternative low carbon emission energy generation becomes crucial for energy systems. The wind power is a popular energy source because of its environmental and economic benefits. However, the uncertainty of wind power, makes its incorporation in energy systems really difficult. To mitigate the risk of demand-supply imbalance by wind power, an accurate estimation of wind power is essential. Recognizing this challenging task, an efficient deep learning based prediction model is proposed for wind power forecasting. In this proposed model, Wavelet Packet Transform (WPT) is used to decompose the wind power signals. Along with decomposed signals and lagged inputs, multiple exogenous inputs (calendar variable, Numerical Weather Prediction (NWP)) are used as input to forecast wind power. Efficient Deep Convolution Neural Network (EDCNN) is employed to forecast wind power. The proposed model’s performance is evaluated on real data of Maine wind farm ISO NE, USA.
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Mujeeb, S., Javaid, N., Gul, H., Daood, N., Shabbir, S., Arif, A. (2020). Wind Power Forecasting Based on Efficient Deep Convolution Neural Networks. In: Barolli, L., Hellinckx, P., Natwichai, J. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2019. Lecture Notes in Networks and Systems, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-030-33509-0_5
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DOI: https://doi.org/10.1007/978-3-030-33509-0_5
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