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A Deep Learning Methodology Based on Bidirectional Gated Recurrent Unit for Wind Power Prediction

Wind power predication is very important for dispatching of power grid and the optimal operation of wind power system. However, the inherent uncertainty of wind makes the power fluctuating, leading to the wind power prediction more difficult. In this paper, a deep learning framework based on bidirectional gated recurrent unit for wind power prediction is proposed to improve the accuracy by making full use of the information provided by multiple data sources of numerical weather forecast. The feasibility of deep learning algorithm to deal with wind power prediction is firstly analyzed, and then, the model based on gated recurrent unit is constructed which can describe the relationship between input information and output information through a gating mechanism. Therefore, it can automatically establish the simplified relationship between wind speed, wind direction and wind power. Then, on the above basis, the model can further describe more complicated and difficult to be quantified from the explicit relationship which is simplified and easy to be quantified to ensure the accuracy of prediction results. Finally, the practical data collected from a wind farm in China has been used to verify the conclusions. Results show that the proposed methodology can predicate the wind power accurately