Day-ahead load probability density forecasting using monotone composite quantile regression neural network and kernel density estimation

Abstract Due to the aggregation of renewable resources and the fluctuation of demand side, exploring more advanced approaches to maintain the accuracy of short-term load forecasting has become a momentous task. The neural network-based quantile regression probability density forecasting can reveal the prediction uncertainty, which is critical in building energy-saving and reliable power grid. However, quantile curves estimated by previous quantile regression probability density forecasting model may cross each other, violating the basic properties of quantile and jeopardizing the flexibility of modeling. For counteracting this disadvantage, a probability density prediction method based on monotone composite quantile regression neural network and Gaussian kernel function (MCQRNNG) for day-ahead load is proposed. The proposed method guarantees non-crossing by simultaneously estimating multiple nonlinear quantile regression functions to obtain better estimation accuracy. Besides, three-dimensional grid search is adopted to adaptively determine the optimal network structure. Taking real load data carrying quantile crossing from Ottawa in Canada, Baden-Wurttemberg in Germany and a certain region in China, the performance of the MCQRNNG model is verified from three aspects of point, interval and probability prediction. Results show that the proposed model significantly outperforms the comparison model on the basis of effectively extricating the quantile crossing for power load forecasting.