English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

China has made great achievements in electric power industry during thelong-term deepening of reform and opening up. However, the complex regionaleconomic, social and natural conditions, electricity resources are not evenlydistributed, which accounts for the electricity deficiency in some regions ofChina. It is desirable to develop a robust electricity forecasting model.Motivated by which, we propose a Panel Semiparametric Quantile RegressionNeural Network (PSQRNN) by utilizing the artificial neural network andsemiparametric quantile regression. The PSQRNN can explore a potential linearand nonlinear relationships among the variables, interpret the unobservedprovincial heterogeneity, and maintain the interpretability of parametricmodels simultaneously. And the PSQRNN is trained by combining the penalizedquantile regression with LASSO, ridge regression and backpropagation algorithm.To evaluate the prediction accuracy, an empirical analysis is conducted toanalyze the provincial electricity consumption from 1999 to 2018 in China basedon three scenarios. From which, one finds that the PSQRNN model performs betterfor electricity consumption forecasting by considering the economic andclimatic factors. Finally, the provincial electricity consumptions of the next$5$ years (2019-2023) in China are reported by forecasting.

Panel semiparametric quantile regression neural network for electricity consumption forecasting