Prediction of daily global solar radiation and air temperature using six machine learning algorithms; a case of 27 European countries

Nematchoua Kameni, Modeste; Kameni Nematchoua, Modeste

doi:10.1016/j.ecoinf.2022.101643

Article (Scientific journals)

Prediction of daily global solar radiation and air temperature using six machine learning algorithms; a case of 27 European countries

Nematchoua Kameni, Modeste; Kameni Nematchoua, Modeste

2022 • In Ecological Informatics

Peer Reviewed verified by ORBi

Permalink
https://hdl.handle.net/2268/289680

DOI
10.1016/j.ecoinf.2022.101643

Files (1)Send to Details Statistics Bibliography Similar publications

Files

Full Text

2022-4.pdf

Author postprint (4.31 MB)

Download

All documents in ORBi are protected by a user license.

Send to

RIS BibTex APA Chicago Permalink X Linkedin

Details

Keywords :

Solar radiation Europe, machine-learning Prediction

Disciplines :

Architecture
Civil engineering
Energy

Author, co-author :

Nematchoua Kameni, Modeste

Kameni Nematchoua, Modeste ; Université de Liège - ULiège > Urban and Environmental Engineering

Language :

English

Title :

Prediction of daily global solar radiation and air temperature using six machine learning algorithms; a case of 27 European countries

Publication date :

15 April 2022

Journal title :

Ecological Informatics

ISSN :

1574-9541

eISSN :

1878-0512

Publisher :

Elsevier, Amsterdam, Netherlands

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 16 April 2022

Statistics

Number of views

66 (3 by ULiège)

Number of downloads

371 (2 by ULiège)

More statistics

OpenCitations