Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting-Reference-Cited by-同舟云学术

Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting

Published:2023-09-21 Issue:18 Volume:16 Page:6731
ISSN:1996-1073
Container-title:Energies
language:en
Short-container-title:Energies

Author:

Harrou Fouzi¹^ORCID,Sun Ying¹,Taghezouit Bilal²,Dairi Abdelkader³^ORCID

Affiliation:

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

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

3. 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 [...]

Funder

King Abdullah University of Science and Technology

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

Link

https://www.mdpi.com/1996-1073/16/18/6731/pdf

Reference11 articles.

1. Castillo-Rojas, W., Medina Quispe, F.M., and Hernández, C. (2023). Photovoltaic Energy Forecast Using Weather Data through a Hybrid Model of Recurrent and Shallow Neural Networks. Energies, 16.

2. Hamied, A., Mellit, A., Benghanem, M., and Boubaker, S. (2023). IoT-Based Low-Cost Photovoltaic Monitoring for a Greenhouse Farm in an Arid Region. Energies, 16.

3. Halassa, E., Mazouz, L., Seghiour, A., Chouder, A., and Silvestre, S. (2023). Revolutionizing Photovoltaic Systems: An Innovative Approach to Maximum Power Point Tracking Using Enhanced Dandelion Optimizer in Partial Shading Conditions. Energies, 16.

4. Polo, J., Martín-Chivelet, N., Alonso-Abella, M., Sanz-Saiz, C., Cuenca, J., and de la Cruz, M. (2023). Exploring the PV Power Forecasting at Building Façades Using Gradient Boosting Methods. Energies, 16.

5. Alfaris, F.E. (2023). A Sensorless Intelligent System to Detect Dust on PV Panels for Optimized Cleaning Units. Energies, 16.

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