Mid-term electricity demand forecasting using improved multi-mode reconstruction and particle swarm-enhanced support vector regression-Reference-Cited by-同舟云学术

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Mid-term electricity demand forecasting using improved multi-mode reconstruction and particle swarm-enhanced support vector regression

Published:2024-09 Issue: Volume:304 Page:132021
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Wang Lei,Wang Xinyu,Zhao Zhongchao^ORCID

Funder

Natural Science Foundation of Shandong Province

Chinese National Funding of Social Sciences

Taishan Scholar Foundation of Shandong Province

National Office for Philosophy and Social Sciences

Taishan Scholar Project of Shandong Province

National Natural Science Foundation of China

Social Science Planning Project of Shandong Province

Publisher

Elsevier BV

Reference56 articles.

1. Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm;Gao;Energy,2022

2. Chinese electricity demand and electricity consumption efficiency: do the structural changes matter?;Lin;Appl Energy,2020

3. Probabilistic load forecasting using post-processed weather ensemble predictions;Ludwig;J Oper Res Soc,2023

4. Research and application of a hybrid model for mid-term power demand forecasting based on secondary decomposition and interval optimization;Niu;Energy,2021

5. A new semiparametric and EEMD based framework for mid-term electricity demand forecasting in China: hidden characteristic extraction and probability density prediction;Shao;Renew Sustain Energy Rev,2015

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