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Enhancing wind and solar energy forecasting through time-series feature engineering and ensemble machine learning.

Overview

Authors: Nouf Abd Elmunim1, Mohamed Arbi Khlifi2, Murdhy A. Aldawsari3, Fahad Algarni4, Abdullah Albalawi5, Atef Ismail6, Basma M. Hassan7
ORCID iDs: Mohamed Arbi Khlifi, Murdhy A. Aldawsari
  1. Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University,P.O. Box 84428, Riyadh, 11671 Saudi Arabia
  2. Department of Electrical Engineering, Faculty of Engineering, Islamic University of Madinah,Madinah, 42351 Saudi Arabia
  3. Nursing Department, College of Applied Medical Sciences in Wade Aldwaser, Prince Sattam Bin Abdulaziz University,Abdulaziz, Saudi Arabia
  4. College of Computing and Information Technology, University of Bisha,Bisha, Saudi Arabia
  5. Department of Computer Science, College of Computing and Information Technology, Shaqra University,Shaqra, Saudi Arabia
  6. Physics Department, Al-Azhar University,Asyut, 71524 Egypt
  7. Faculty of Artificial Intelligence, Kafrelsheikh University,Kafrelsheikh, 33516 Egypt
Institutions: Princess Nourah bint Abdulrahman University (Saudi Arabia); Islamic University of Madinah (Saudi Arabia); Prince Sattam Bin Abdulaziz University (Saudi Arabia); University of Bisha (Saudi Arabia); Shaqra University (Saudi Arabia); Al-Azhar University (Egypt); Kafrelsheikh University (Egypt)
Journal: Scientific reports, volume 16, issue 1, article 15546
Dates: received 31 January 2026; accepted 14 April 2026; published online 19 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-49373-7 · PMID 42156795 · PMCID PMC13187037 · OpenAlex W7161687266
Open access: gold, a free copy (OpenAlex)
Status: code on request
Methods: Machine learning, Connectivity, Smoothing, state filtering, decompositions, Spectral & time-frequency, Physiology & signal measures
Keywords: Energy production forecasting, Machine learning, Ensemble regression, CatBoost, Time series features, Renewable energy, Hourly prediction, Energy analytics, Energy science and technology, Engineering, Mathematics and computing
Topic: Energy Load and Power Forecasting (Electrical and Electronic Engineering, Engineering), according to OpenAlex
Citations: cited by 1 paper (Europe PMC); 18 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

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The paper's code and data availability statement is in the Data section.

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Data

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Code and data availability statement

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  • it says that the data are available on request
  • it says that the code is available on request

Read it in the paper: doi.org/10.1038/s41598-026-49373-7.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 11 keywords, 17 references.

Cite

This paper

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, 16(1), 15546. https://doi.org/10.1038/s41598-026-49373-7

BibTeX

@article{elmunim2026enhancing,
author = {Elmunim, Nouf Abd and Khlifi, Mohamed Arbi and Aldawsari, Murdhy A. and Algarni, Fahad and Albalawi, Abdullah and Ismail, Atef and Hassan, Basma M.},
title = {{Enhancing wind and solar energy forecasting through time-series feature engineering and ensemble machine learning}},
journal = {Scientific reports},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {15546},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-49373-7},
url = {https://doi.org/10.1038/s41598-026-49373-7},
pmid = {42156795},
pmcid = {PMC13187037}
}

RIS

TY - JOUR
AU - Elmunim, Nouf Abd
AU - Khlifi, Mohamed Arbi
AU - Aldawsari, Murdhy A.
AU - Algarni, Fahad
AU - Albalawi, Abdullah
AU - Ismail, Atef
AU - Hassan, Basma M.
TI - Enhancing wind and solar energy forecasting through time-series feature engineering and ensemble machine learning
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/05/19
VL - 16
IS - 1
SP - 15546
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-49373-7
UR - https://doi.org/10.1038/s41598-026-49373-7
LA - en
ER -

CSL-JSON

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"issued": {
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