Conformal asymmetric multi-quantile generative transformer for day-ahead wind power interval prediction

With the rapid increase in the installed capacity of wind power, day-ahead wind power interval prediction is becoming more and more important. To solve such a challenging problem and provide intervals of higher quality, this paper proposes a prediction method based on conformal asymmetric multi-quantile generative transformer. Herein, the multi-quantile generative transformer is a deep learning model that generates multi-quantile forecast results for next day through one forward propagation process. Then, two quantiles, whose width is the smallest while satisfying the nominal confidence constraint, are selected from the predicted sequence as the upper bound and lower bound of the asymmetric interval. Furthermore, we introduce the conformal quantile regression to calibrate the bounds of the prediction interval to ensure that its coverage rate is as close as nominal confidence. The experiments show that the proposed method surpasses the benchmarks by providing narrower prediction intervals with more accurate empirical coverage probability. Under nominal confidence 90%, it gives prediction intervals with average empirical coverage probability of 90.50% and normalized average width of 0.44 on four wind farms. Compared with symmetric prediction intervals given by common benchmark quantile long short term memory network, the average width is reduced by 19.6%.

Automated summary

With the rapid increase in wind power installed capacity, day-ahead wind power interval prediction has become increasingly important. This paper proposes a prediction method based on conformal asymmetric multi-quantile generative transformer to provide higher quality intervals. The experiments show that this method outperforms benchmarks by providing narrower prediction intervals with more accurate empirical coverage probability. The average width is reduced by 19.6% compared to symmetric prediction intervals given by common benchmark quantile long short term memory network.