Panel semiparametric quantile regression neural network for electricity consumption forecasting
نویسندگان
چکیده
Addressing the forecasting issues is one of core objectives developing and restructuring electric power industry in China. However, there are not enough efforts that have been made to develop an accurate electricity consumption procedure. In this paper, a panel semiparametric quantile regression neural network (PSQRNN) developed by combining artificial for data. By embedding penalized with least absolute shrinkage selection operator (LASSO), ridge backpropagation, PSQRNN keeps flexibility nonparametric models interpretability parametric simultaneously. The prediction accuracy evaluated based on China's data set, results indicate performs better compared three benchmark methods including BP (BP), Support Vector Machine (SVM) Quantile Regression Neural Network (QRNN).
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ژورنال
عنوان ژورنال: Ecological Informatics
سال: 2022
ISSN: ['1878-0512', '1574-9541']
DOI: https://doi.org/10.1016/j.ecoinf.2021.101489