Optimizing solar power forecasting with metaheuristic algorithms and deep learning models for photovoltaic grid connected systems
- DOI
- 10.1038/s41598-025-23822-1
- Published
- 2025-11-14
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1038/s41598-025-23822-1,
title = {Optimizing solar power forecasting with metaheuristic algorithms and deep learning models for photovoltaic grid connected systems},
author = {Putri Nor Liyana Mohamad Radzi and Saad Mekhilef and Noraisyah Mohamed Shah and Muhammad Naveed Akhter and Mehdi Seyedmahmoudian and Alex Stojcevski},
year = {2025},
journal = {Scientific Reports},
doi = {10.1038/s41598-025-23822-1},
url = {https://doi.org/10.1038/s41598-025-23822-1}
}RIS
TY - JOUR TI - Optimizing solar power forecasting with metaheuristic algorithms and deep learning models for photovoltaic grid connected systems AU - Putri Nor Liyana Mohamad Radzi AU - Saad Mekhilef AU - Noraisyah Mohamed Shah AU - Muhammad Naveed Akhter AU - Mehdi Seyedmahmoudian AU - Alex Stojcevski PY - 2025 JO - Scientific Reports DO - 10.1038/s41598-025-23822-1 UR - https://doi.org/10.1038/s41598-025-23822-1 ER -
APA
Radzi, P. N. L. M., Mekhilef, S., Shah, N. M., Akhter, M. N., Seyedmahmoudian, M., & Stojcevski, A. (2025). Optimizing solar power forecasting with metaheuristic algorithms and deep learning models for photovoltaic grid connected systems. Scientific Reports. https://doi.org/10.1038/s41598-025-23822-1
Source records
- crossref · retrieved 2026-09-25T21:05:20.543Z