Deep Learning-Based Short-Term Wind Power Prediction Considering Various Factors

Owing to the increasing penetration of offshore wind energy in smart grids, accurate forecasting plays a significant role in energy improvement and economic dispatch. However, owing to the intermittent and uncertain nature of wind power, traditional numerical weather prediction methods can hardly capture the fluctuation caused by wind power. This paper proposes a deep- learning based forecasting algorithm to predict offshore wind power under consideration of various environmental factors. In order to reduce forecasting error, an ensemble strategy is utilized to improve the prediction performance. The developed model has been practically tested on an offshore wind farm in Nantong, China. The forecasting results demonstrate the high accuracy and quality of the proposed method for wind power prediction, which provides an efficient reference to optimal power system operation.