Electricity Consumption Forecasting by a New Neural Network Model: Panel Semiparametric Quantile Regression Neural Network (PSQRNN)

• Activity Number: 244 - Advances in Statistical Machine Learning
• Type: Contributed
• Date/Time: Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
• Sponsor: IMS
• Author(s): Jiangyan Wang* and Xingcai Zhou and Hongxia Wang and Jinguan Lin
• Companies: Nanjing Audit University and Nanjing Audit University and Nanjing Audit University and Nanjing Audit University
• Keywords: Electricity consumption forecasting; ; Panel data; Semiparametric quantile regression;; Artificial neural network; PSQRNN

Abstract

Addressing the forecasting issues is one of the core objectives of developing and restructuring of electric power industry in China, however, no enough effort has been made to develop an accurate electricity consumption forecasting procedure. Motivated by which, a panel semiparametric quantile regression neural network (PSQRNN) is developed by combining an artificial neural network and semiparametric quantile regression for panel data. By combining the 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 of the proposed PSQRNN 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).