Comparison of machine learning and deep learning algorithms for hourly global/diffuse solar radiation predictions-Reference-Cited by-同舟云学术

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Comparison of machine learning and deep learning algorithms for hourly global/diffuse solar radiation predictions

Published:2021-02-16 Issue: Volume: Page:
ISSN:0363-907X
Container-title:International Journal of Energy Research
language:en
Short-container-title:Intl J of Energy Research

Author:

Bamisile Olusola¹^ORCID,Oluwasanmi Ariyo²,Ejiyi Chukwuebuka²,Yimen Nasser³,Obiora Sandra⁴,Huang Qi¹⁵

Affiliation:

1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China Chengdu China

2. School of Software Engineering, University of Electronic Science and Technology of China Chengdu China

3. National Advanced School of Engineering, University of Yaounde I Yaounde Cameroon

4. School of Management and Economics, University of Electronic Science and Technology of China Chengdu China

5. College of Nuclear Technology and Automation Engineering, Chengdu University of Technology Chengdu China

Publisher

Wiley

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/er.6529

Reference65 articles.

1. DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States

2. Evaluation of temperature-based machine learning and empirical models for predicting daily global solar radiation

3. Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China

4. Machine learning regressors for solar radiation estimation from satellite data

5. Machine learning models to quantify and map daily global solar radiation and photovoltaic power

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