Deep learning based ensemble approach for probabilistic wind power forecasting

Abstract

Due to the economic and environmental benefits, wind power is becoming one of the more promising supplements for electric power generation. However, the uncertainty exhibited in wind power data is generally unacceptably large. Thus, the data should be accurately evaluated by operators to effectively mitigate the risks of wind power on power system operations. Recognizing this challenge, a novel deep learning based ensemble approach is proposed for probabilistic wind power forecasting. In this approach, an advanced point forecasting method is originally proposed based on wavelet transform and convolutional neural network. Wavelet transform is used to decompose the raw wind power data into different frequencies. The nonlinear features in each frequency that are used to improve the forecast accuracy are later effectively learned by the convolutional neural network. The uncertainties in wind power data, i.e., the model misspecification and data noise, are separately identified thereafter. Consequently, the probabilistic distribution of wind power data can be statistically formulated. The proposed ensemble approach has been extensively assessed using real wind farm data from China, and the results demonstrate that the uncertainties in wind power data can be better learned using the proposed approach and that a competitive performance is obtained.