An Experimental Machine Learning Approach for Mid-Term Energy Demand Forecasting-Reference-Cited by-同舟云学术

An Experimental Machine Learning Approach for Mid-Term Energy Demand Forecasting

Published:2022 Issue: Volume:10 Page:118926-118940
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Yan Shu-Rong¹,Tian Manwen²,Alattas Khalid A.³^ORCID,Mohamadzadeh Ardashir⁴^ORCID,Sabzalian Mohammad Hosein⁵,Mosavi Amir H.⁶^ORCID

Affiliation:

1. School of Business Administration, Hunan University, Changsha, China

2. National Key Project Laboratory, Jiangxi University of Engineering, Xinyu, China

3. Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia

4. Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang, China

5. Laboratory of Power Electronics and Medium Voltage Applications (LEMT), The Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil

6. Obuda University, Budapest, Hungary

Funder

Project of the National Social Science Fundation

Fujian Social Science Foundation Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09945969.pdf?arnumber=9945969

Reference37 articles.

1. Pearson Correlation Coefficient