Enhancing wind and solar energy forecasting through time-series feature engineering and ensemble machine learning
- DOI
- 10.1038/s41598-026-49373-7
- Published
- 2026-05-19
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-49373-7,
title = {Enhancing wind and solar energy forecasting through time-series feature engineering and ensemble machine learning},
author = {Nouf Abd Elmunim and Mohamed Arbi Khlifi and Murdhy A. Aldawsari and Fahad Algarni and Abdullah Albalawi and Atef Ismail and Basma M. Hassan},
year = {2026},
journal = {Scientific Reports},
doi = {10.1038/s41598-026-49373-7},
url = {https://doi.org/10.1038/s41598-026-49373-7}
}RIS
TY - JOUR TI - Enhancing wind and solar energy forecasting through time-series feature engineering and ensemble machine learning AU - Nouf Abd Elmunim AU - Mohamed Arbi Khlifi AU - Murdhy A. Aldawsari AU - Fahad Algarni AU - Abdullah Albalawi AU - Atef Ismail AU - Basma M. Hassan PY - 2026 JO - Scientific Reports DO - 10.1038/s41598-026-49373-7 UR - https://doi.org/10.1038/s41598-026-49373-7 ER -
APA
Elmunim, N. A., Khlifi, M. A., Aldawsari, M. A., Algarni, F., Albalawi, A., Ismail, A., & Hassan, B. M. (2026). Enhancing wind and solar energy forecasting through time-series feature engineering and ensemble machine learning. Scientific Reports. https://doi.org/10.1038/s41598-026-49373-7
Source records
- crossref · retrieved 2026-09-25T23:24:17.832Z