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Short-Term Wind Power Ensemble Prediction Based on Gaussian Processes and Neural Networks

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2 Author(s)
Duehee Lee ; Electr. & Comput. Eng., Univ. of Texas at Austin, Austin, TX, USA ; Ross Baldick

We propose an ensemble short-term wind power forecasting model that is based on our novel approaches and advanced forecasting techniques in the modern literature. The performance of our model has been verified by forecasting wind power up to 48 hours ahead at seven wind farms for one and a half years. Our model ranked fourth in the Power and Energy Society (PES) wind power forecasting competition. The forecasting model uses 52 Neural Network (NN) sub-models and five Gaussian Process (GP) sub-models in parallel. For 48 hours, the NN sub-models forecast the future wind power based on historical wind power data and forecasted wind information. In parallel, for the first five hours, five GP sub-models are used to forecast wind power using only historical wind power in order to provide accurate wind power forecasts to NN sub-models. These models provide various forecasts for the same hour, so the optimal forecast should be decided from overlapped forecasts by the decision process.

Published in:

IEEE Transactions on Smart Grid  (Volume:5 ,  Issue: 1 )