Comparative Study of Feature Selection Techniques for Machine Learning-Based Solar Irradiation Forecasting to Facilitate the Sustainable Development of Photovoltaics: Application to Algerian Climatic Conditions
- DOI
- 10.3390/su17146400
- Published
- 2025-07-12
- Container
- Sustainability
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/su17146400,
title = {Comparative Study of Feature Selection Techniques for Machine Learning-Based Solar Irradiation Forecasting to Facilitate the Sustainable Development of Photovoltaics: Application to Algerian Climatic Conditions},
author = {Said Benkaciali and Gilles Notton and Cyril Voyant},
year = {2025},
journal = {Sustainability},
doi = {10.3390/su17146400},
url = {https://doi.org/10.3390/su17146400}
}RIS
TY - JOUR TI - Comparative Study of Feature Selection Techniques for Machine Learning-Based Solar Irradiation Forecasting to Facilitate the Sustainable Development of Photovoltaics: Application to Algerian Climatic Conditions AU - Said Benkaciali AU - Gilles Notton AU - Cyril Voyant PY - 2025 JO - Sustainability DO - 10.3390/su17146400 UR - https://doi.org/10.3390/su17146400 ER -
APA
Benkaciali, S., Notton, G., & Voyant, C. (2025). Comparative Study of Feature Selection Techniques for Machine Learning-Based Solar Irradiation Forecasting to Facilitate the Sustainable Development of Photovoltaics: Application to Algerian Climatic Conditions. Sustainability. https://doi.org/10.3390/su17146400
Source records
- crossref · retrieved 2026-11-09T02:08:00.751Z