A day-ahead industrial load forecasting model using load change rate features and combining FA-ELM and the AdaBoost algorithm-Reference-Cited by-同舟云学术

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A day-ahead industrial load forecasting model using load change rate features and combining FA-ELM and the AdaBoost algorithm

Published:2023-12 Issue: Volume:9 Page:971-981
ISSN:2352-4847
Container-title:Energy Reports
language:en
Short-container-title:Energy Reports

Author:

Zhu Ziwei^ORCID,Zhou Mengran^ORCID,Hu Feng^ORCID,Wang Shenghe,Ma Jinhui,Gao Bo,Bian Kai,Lai Wenhao

Publisher

Elsevier BV

Subject

General Energy

Reference42 articles.

1. Dwarf mongoose optimization algorithm;Agushaka;Comput. Methods Appl. Mech. Engrg.,2022

2. Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecasting;Al-Musaylh;Appl. Energy,2018

3. Short-term wind power prediction based on particle swarm optimization-extreme learning machine model combined with AdaBoost algorithm;An;IEEE Access,2021

4. Electrical load forecasting models for different generation modalities: a review;Azeem;IEEE Access,2021

5. Adaptive firefly algorithm with chaos for mechanical design optimization problems;Baykasoğlu;Appl. Soft Comput.,2015

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