Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory-Reference-Cited by-同舟云学术

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Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory

Published:2017-01 Issue: Volume:185 Page:254-266
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

He Yaoyao,Liu Rui,Li Haiyan,Wang Shuo,Lu Xiaofen

Funder

National Natural Science Foundation

AnhuiProvincialNaturalScienceFoundation

Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin

Specialized Research Fund for the Doctoral Program of Higher Education

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference49 articles.

1. Short-term load forecasting based on a semi-parametric additive model;Fan;IEEE Trans Power Syst,2012

2. Improving photovoltaics grid integration through short time forecasting and self-consumption;Masa-Bote;Appl Energy,2014

3. Comprehensive review of neural network-based prediction intervals and new advances;Khosravi;IEEE Trans Neural Networks,2011

4. Calculating interval forecasts;Chatfield;J Bus Econ Stat,1993

5. Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function;He;Energy,2016

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