Machine Learning Techniques for Forecasting Daily Global Solar Radiation Ahead-Reference-Cited by-同舟云学术

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Machine Learning Techniques for Forecasting Daily Global Solar Radiation Ahead

Published:2023-12-16 Issue: Volume: Page:1-6
ISSN:
Container-title:2023 14th International Renewable Energy Congress (IREC)
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Author:

Takilalte A.¹,Laissaoui M.¹,Bouhallassa A.¹,Mora-López L.²,Harrouni S.³

Affiliation:

1. Centre de Développement des Energies Renouvelables, CDER BP 62 Route de l'Observatoire, Bouzaréah,Algiers,Algeria,16340

2. Faculte de Genie Electrique Universite des Sciences et de la Technologie Houari Boumediene, USTHB BP32 El Alia,Algiers,Algeria,16111

3. Universidad de Málaga Campus de Teatinos,Departamento de Lenguajes y Ciencias de la Computación, E.T.S.I. Informática,Malaga,Spain,29071

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10389178/10389179/10389357.pdf?arnumber=10389357

Reference14 articles.

1. Evaluating solar radiation forecast uncertainty

2. A critical review of the models used to estimate solar radiation

3. Binding data mining and expert knowledge for one-day-ahead prediction of hourly global solar radiation

4. Solar irradiance forecasting models using machine learning techniques and digital twin: A case study with comparison

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

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