  China has made great achievements in electric power industry during the
long-term deepening of reform and opening up. However, the complex regional
economic, social and natural conditions, electricity resources are not evenly
distributed, which accounts for the electricity deficiency in some regions of
China. It is desirable to develop a robust electricity forecasting model.
Motivated by which, we propose a Panel Semiparametric Quantile Regression
Neural Network (PSQRNN) by utilizing the artificial neural network and
semiparametric quantile regression. The PSQRNN can explore a potential linear
and nonlinear relationships among the variables, interpret the unobserved
provincial heterogeneity, and maintain the interpretability of parametric
models simultaneously. And the PSQRNN is trained by combining the penalized
quantile regression with LASSO, ridge regression and backpropagation algorithm.
To evaluate the prediction accuracy, an empirical analysis is conducted to
analyze the provincial electricity consumption from 1999 to 2018 in China based
on three scenarios. From which, one finds that the PSQRNN model performs better
for electricity consumption forecasting by considering the economic and
climatic factors. Finally, the provincial electricity consumptions of the next
$5$ years (2019-2023) in China are reported by forecasting.
