Two-stage short-term wind power probabilistic prediction using natural gradient boosting combined with neural network

Abstract

Accurate wind power probabilistic prediction reflects the uncertainty information of wind power generation, which is the foundation for optimal scheduling of power systems. This study proposes a two-stage probabilistic prediction model combining natural gradient boosting and neural network for accurate uncertainty estimation of short-term output in a wind farm. In the first stage, the selected input features containing historical and future information are fed into a neural network for representation learning. In the second stage, the extracted abstract features are concatenated with the original features, and a natural gradient boosting model is employed to acquire short-term probabilistic forecasts. The experimental results using data from two real wind farms indicate that the proposed hybrid model can generate accurate, sharp, and reliable forecasts. After performing the successive day-ahead prediction task for a month in the first wind farm, the average root mean square error and mean absolute error of the proposed model in the point prediction were 0.1330 and 0.1070, respectively, which were 6.21%–57.29% and 2.96%–62.03% lower than those of comparative models. In addition, the model’s forecasting probability density curves demonstrate high reliability; its coverage probability and the mean width percentage of the interval prediction results under the 90% confidence level were 0.9094 and 0.3696, respectively, which were more suitable than those of five other probabilistic prediction models.