IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i18p6731-d1244326.html
   My bibliography  Save this article

Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting

Author

Listed:
  • Fouzi Harrou

    (Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia)

  • Ying Sun

    (Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia)

  • Bilal Taghezouit

    (Centre de Développement des Energies Renouvelables, CDER, B.P. 62, Route de l’Observatoire, Algiers 16340, Algeria)

  • Abdelkader Dairi

    (Computer Science Department, University of Science and Technology of Oran-Mohamed Boudiaf, USTO-MB, BP 1505, El Mnaouar, Bir El Djir 10587, Algeria)

Abstract

Solar Photovoltaic (PV) systems represent key and transformative technology at the forefront of the global shift towards sustainable energy solutions [...]

Suggested Citation

  • Fouzi Harrou & Ying Sun & Bilal Taghezouit & Abdelkader Dairi, 2023. "Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting," Energies, MDPI, vol. 16(18), pages 1-5, September.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6731-:d:1244326
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/18/6731/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/18/6731/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Elmamoune Halassa & Lakhdar Mazouz & Abdellatif Seghiour & Aissa Chouder & Santiago Silvestre, 2023. "Revolutionizing Photovoltaic Systems: An Innovative Approach to Maximum Power Point Tracking Using Enhanced Dandelion Optimizer in Partial Shading Conditions," Energies, MDPI, vol. 16(9), pages 1-23, April.
    2. Fouzi Harrou & Bilal Taghezouit & Sofiane Khadraoui & Abdelkader Dairi & Ying Sun & Amar Hadj Arab, 2022. "Ensemble Learning Techniques-Based Monitoring Charts for Fault Detection in Photovoltaic Systems," Energies, MDPI, vol. 15(18), pages 1-28, September.
    3. Jesús Polo & Nuria Martín-Chivelet & Miguel Alonso-Abella & Carlos Sanz-Saiz & José Cuenca & Marina de la Cruz, 2023. "Exploring the PV Power Forecasting at Building Façades Using Gradient Boosting Methods," Energies, MDPI, vol. 16(3), pages 1-12, February.
    4. Sajid Sarwar & Muhammad Annas Hafeez & Muhammad Yaqoob Javed & Aamer Bilal Asghar & Krzysztof Ejsmont, 2022. "A Horse Herd Optimization Algorithm (HOA)-Based MPPT Technique under Partial and Complex Partial Shading Conditions," Energies, MDPI, vol. 15(5), pages 1-22, March.
    5. Faris E. Alfaris, 2023. "A Sensorless Intelligent System to Detect Dust on PV Panels for Optimized Cleaning Units," Energies, MDPI, vol. 16(3), pages 1-17, January.
    6. Wilson Castillo-Rojas & Fernando Medina Quispe & César Hernández, 2023. "Photovoltaic Energy Forecast Using Weather Data through a Hybrid Model of Recurrent and Shallow Neural Networks," Energies, MDPI, vol. 16(13), pages 1-25, July.
    7. Jingwei Zhang & Zenan Yang & Kun Ding & Li Feng & Frank Hamelmann & Xihui Chen & Yongjie Liu & Ling Chen, 2022. "Modeling of Photovoltaic Array Based on Multi-Agent Deep Reinforcement Learning Using Residuals of I–V Characteristics," Energies, MDPI, vol. 15(18), pages 1-17, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Latifa A. Yousef & Hibba Yousef & Lisandra Rocha-Meneses, 2023. "Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions," Energies, MDPI, vol. 16(24), pages 1-27, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bilal Taghezouit & Fouzi Harrou & Cherif Larbes & Ying Sun & Smail Semaoui & Amar Hadj Arab & Salim Bouchakour, 2022. "Intelligent Monitoring of Photovoltaic Systems via Simplicial Empirical Models and Performance Loss Rate Evaluation under LabVIEW: A Case Study," Energies, MDPI, vol. 15(21), pages 1-30, October.
    2. Yu Fujimoto & Akihisa Kaneko & Yutaka Iino & Hideo Ishii & Yasuhiro Hayashi, 2023. "Challenges in Smartizing Operational Management of Functionally-Smart Inverters for Distributed Energy Resources: A Review on Machine Learning Aspects," Energies, MDPI, vol. 16(3), pages 1-26, January.
    3. Domenico Palladino & Nicolandrea Calabrese, 2023. "Energy Planning of Renewable Energy Sources in an Italian Context: Energy Forecasting Analysis of Photovoltaic Systems in the Residential Sector," Energies, MDPI, vol. 16(7), pages 1-28, March.
    4. Jose Cruz & Christian Romero & Oscar Vera & Saul Huaquipaco & Norman Beltran & Wilson Mamani, 2023. "Multiparameter Regression of a Photovoltaic System by Applying Hybrid Methods with Variable Selection and Stacking Ensembles under Extreme Conditions of Altitudes Higher than 3800 Meters above Sea Lev," Energies, MDPI, vol. 16(12), pages 1-21, June.
    5. Elmamoune Halassa & Lakhdar Mazouz & Abdellatif Seghiour & Aissa Chouder & Santiago Silvestre, 2023. "Revolutionizing Photovoltaic Systems: An Innovative Approach to Maximum Power Point Tracking Using Enhanced Dandelion Optimizer in Partial Shading Conditions," Energies, MDPI, vol. 16(9), pages 1-23, April.
    6. Anupama Ganguly & Pabitra Kumar Biswas & Chiranjit Sain & Ahmad Taher Azar & Ahmed Redha Mahlous & Saim Ahmed, 2023. "Horse Herd Optimized Intelligent Controller for Sustainable PV Interface Grid-Connected System: A Qualitative Approach," Sustainability, MDPI, vol. 15(14), pages 1-26, July.
    7. Chanuri Charin & Dahaman Ishak & Muhammad Ammirrul Atiqi Mohd Zainuri & Baharuddin Ismail & Turki Alsuwian & Adam R. H. Alhawari, 2022. "Modified Levy-based Particle Swarm Optimization (MLPSO) with Boost Converter for Local and Global Point Tracking," Energies, MDPI, vol. 15(19), pages 1-30, October.
    8. Benamar Bouyeddou & Fouzi Harrou & Bilal Taghezouit & Ying Sun & Amar Hadj Arab, 2022. "Improved Semi-Supervised Data-Mining-Based Schemes for Fault Detection in a Grid-Connected Photovoltaic System," Energies, MDPI, vol. 15(21), pages 1-22, October.
    9. Izabela Rojek & Dariusz Mikołajewski & Adam Mroziński & Marek Macko, 2023. "Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage," Energies, MDPI, vol. 16(18), pages 1-26, September.

    More about this item

    Keywords

    n/a;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6731-:d:1244326. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.