Panel semiparametric quantile regression neural network for electricity consumption forecasting |
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| Institution: | 1. CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun 666303, China;2. Center for Plant Ecology, Core Botanical Gardens, Chinese Academy of Sciences, Xishuangbanna 666303, China;3. Global Change Research Group, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun 666303, China;4. Department of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China;1. CREA, Research Centre for Forestry and Wood, Viale Santa Margherita 80, Arezzo IT-52100, Italy;2. ERSAF, Ente Regionale per i Servizi all''Agricoltura e alle Foreste, Via Pola 12, Milan IT-20124, Italy;3. Flysight S.r.l., Via A. Lampredi 45, Livorno IT-57121, Italy;4. CREA, Research Centre for Forestry and Wood, Via Valle della Quistione 27, Roma IT-00166, Italy;1. Department of Biology, University of Maryland, College Park, MD, USA;2. Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, USA;3. CEFE, Univ Montpellier, CNRS, EPHE, Univ Paul-Valery Montpellier, Montpellier, France;1. Departamento de Ciencias Integradas, Facultad de Ciencias Experimentales, Universidad de Huelva, 21007 Huelva, Spain;2. Departamento de Tecnologías de la Información, Escuela Técnica Superior de Ingeniería, Universidad de Huelva, 21007 Huelva, Spain;3. Departamento de Sistemas de Visión, Predicción, Optimización y Control del Centro Científico y Tecnológico de Huelva, Universidad de Huelva, 21007 Huelva, Spain |
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| Abstract: | Addressing the forecasting issues is one of the core objectives of developing and restructuring of electric power industry in China. However, there are not enough efforts that have been made to develop an accurate electricity consumption forecasting procedure. In this paper, a panel semiparametric quantile regression neural network (PSQRNN) is developed by combining an artificial neural network and semiparametric quantile regression for panel data. By embedding 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 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). |
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| Keywords: | Electricity consumption forecasting Panel data Semiparametric quantile regression Artificial neural network PSQRNN |
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