Day-Ahead Probabilistic Load Forecasting: A Multi-Information Fusion and Noncrossing Quantiles Method
Yu Huang, Haode Guo, Engang Tian, Hongtian Chen
Abstract
Probability forecasting is a powerful tool for quantifying uncertainty in short-term load forecasting. However, its performance may be hampered by excessive feature redundancy and the quantile crossing phenomenon. To overcome these challenges, this study proposes a novel deep noncrossing quantile method with multi-information fusion for day-ahead load probabilistic density forecasting. This method extracts different types of input features through distinct neural networks, and can reduce the redundancy of feature information. Based on the positive differences among output values from neural networks, a novel quantile noncrossing strategy is introduced. This strategy, integrated within the neural network, eliminates quantile crossing phenomena and enhances the interpretability of model during the training process. Experimental results show that the proposed model reduces the quantile loss by 11% to 31%, produces prediction intervals with higher quality, precision, and no crossovers.