Load forecasting based on deep neural network and historical data augmentation-Reference-Cited by-同舟云学术

Load forecasting based on deep neural network and historical data augmentation

Published:2020-09-18 Issue:24 Volume:14 Page:5927-5934
ISSN:1751-8687
Container-title:IET Generation, Transmission & Distribution
language:en
Short-container-title:IET Generation, Transmission & Distribution

Author:

Lai Chun Sing¹²^ORCID,Mo Zhenyao³,Wang Ting³,Yuan Haoliang¹,Ng Wing W.Y.³,Lai Loi Lei¹^ORCID

Affiliation:

1. Department of Electrical EngineeringSchool of AutomationGuangdong University of TechnologyGuangzhou510006People's Republic of China

2. Brunel Institute of Power SystemsDepartment of Electronic and Computer EngineeringBrunel University LondonLondonUB8 3PHUK

3. Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace InformationSchool of Computer Science and EngineeringSouth China University of TechnologyGuangzhou510630People's Republic of China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-gtd.2020.0842

Reference35 articles.

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4. Robust power system security assessment under uncertainties using Bi‐level optimization;Yorino N.;IEEE Trans. Power Syst.,2018

5. An accurate and fast converging short‐term load forecasting model for industrial applications in a smart grid;Ahmad A.;IEEE Trans. Ind. Inf.,2017

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