Panel quantile regression neural network for electricity consumption forecasting in China: a new framework

ABSTRACT

Accurate electricity consumption forecasting (ECF) is challenging due to its complexity, and it is more challenging for a provincial ECF in China when it comes to the great heterogeneity among provinces. To depict and predict electricity consumption, a new framework, the panel quantile regression neural network (PQRNN), is developed by adding an artificial neural network structure to a panel quantile regression model. The PQRNN can account for the complex nonlinear relationship and the latent provincial heterogeneity simultaneously. In addition, a differential approximation of the quantile loss function and a quasi-Newton optimization based on the backpropagation algorithm is developed. The prediction accuracy is evaluated by an empirical analysis of the provincial panel dataset from 1999 to 2017 in China, which shows that the ECF based on the PQRNN performs well. Finally, the provincial electricity consumptions over the next 5 years (2018–2022) are predicted by utilizing the PQRNN model.

KEYWORDS:

• Panel quantile regression neural network
• panel data
• electricity consumption forecasting
• penalized QR
• artificial neural network
• PQRNN

Acknowledgments

The authors are truly grateful to the Editor-in-Chief Professor Dr. Alexandros Flamos and the anonymous referees for their comments. This work is supported by the National Social Science Fund of China (No. 19BTJ034).

Declaration of interest

none

Additional information

Funding

This work was supported by the National Social Science Fund of China [19BTJ034].