Panel semiparametric quantile regression neural network for electricity consumption forecasting.

• PSQRNN is proposed for the regional electricity consumption forecasting, which combines panel data, semiparametric model and QRNN (quantile regression neural networks). • PSQRNN is a new framework exploring the potential linear and nonlinear relationship and maintaining a better interpretability of parametric models. • PSQRNN is trained by assembling the penalized quantile regression with LASSO, ridge regression and backpropagation algorithm. Addressing the forecasting issues is one of the core objectives of developing and restructuring of electric power industry in China. However, there are not enough efforts that have been made to develop an accurate electricity consumption forecasting procedure. In this paper, a panel semiparametric quantile regression neural network (PSQRNN) is developed by combining an artificial neural network and semiparametric quantile regression for panel data. By embedding penalized quantile regression with least absolute shrinkage and selection operator (LASSO), ridge regression and backpropagation, PSQRNN keeps the flexibility of nonparametric models and the interpretability of parametric models simultaneously. The prediction accuracy is evaluated based on China's electricity consumption data set, and the results indicate that PSQRNN performs better compared with three benchmark methods including BP neural network (BP), Support Vector Machine (SVM) and Quantile Regression Neural Network (QRNN). [ABSTRACT FROM AUTHOR]