Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison-Reference-Cited by-同舟云学术

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Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison

Published:2021-01 Issue: Volume:135 Page:110114
ISSN:1364-0321
Container-title:Renewable and Sustainable Energy Reviews
language:en
Short-container-title:Renewable and Sustainable Energy Reviews

Author:

Ağbulut Ümit,Gürel Ali Etem,Biçen Yunus

Funder

Düzce University Scientific Research Projects Coordination Unit

Publisher

Elsevier BV

Subject

Renewable Energy, Sustainability and the Environment

Reference91 articles.

1. Performance enhancement of a greenhouse dryer: analysis of a cost-effective alternative solar air heater;Khanlari;J Clean Prod,2020

2. Designing and modeling solar energy systems;Kalogirou,2014

3. Drying municipal sewage sludge with v-groove triple-pass and quadruple-pass solar air heaters along with testing of a solar absorber drying chamber;Khanlari;Sci Total Environ,2020

4. Estimating daily Global solar radiation with graphical user interface in Eastern Mediterranean region of Turkey;Yıldırım;Renew Sustain Energy Rev,2018

5. Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models;Yadav;Renew Sustain Energy Rev,2014

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