Evidence map›Paper›PMID 40595136›Full record

ArticleScientific reports2025

Enhancing PV power forecasting through feature selection and artificial neural networks: a case study.

Mokhtar Ali, Abdelhalim Rabehi, Abdelkerim Souahlia, Mawloud Guermoui, Ali Teta, Imad Eddine Tibermacine, Abdelaziz Rabehi, Mohamed Benghanem, Takele Ferede Agajie

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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Mokhtar AliTelecommunications and Smart Systems Laboratory, University of Djelfa, P.O. Box 3117, 17000, Djelfa, Algeria.
Abdelhalim RabehiTelecommunications and Smart Systems Laboratory, University of Djelfa, P.O. Box 3117, 17000, Djelfa, Algeria.
Abdelkerim SouahliaTelecommunications and Smart Systems Laboratory, University of Djelfa, P.O. Box 3117, 17000, Djelfa, Algeria.
Mawloud GuermouiUnité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement des Energies Renouvelables, CDER, 47133, Ghardaïa, Algeria.
Ali TetaTelecommunications and Smart Systems Laboratory, University of Djelfa, P.O. Box 3117, 17000, Djelfa, Algeria.
Imad Eddine TibermacineDepartment of Computer, Control, and Management Engineering, Sapienza University of Rome, 00185, Rome, Italy.
Abdelaziz RabehiTelecommunications and Smart Systems Laboratory, University of Djelfa, P.O. Box 3117, 17000, Djelfa, Algeria.
Mohamed BenghanemPhysics Department, Faculty of Science, Islamic University of Madinah, 42351, Madinah, Saudi Arabia.
Takele Ferede AgajieDepartment of Electrical and Computer Engineering, Institute of Technology, Debre Markos University, P.O. Box 269, Debre Markos, Ethiopia. takele_ferede@dmu.edu.et.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents a comprehensive investigation into enhancing photovoltaic (PV) power forecasting by systematically integrating feature selection techniques with artificial neural networks. Addressing the growing demand for reliable renewable energy forecasting, the study employs several feature selection methods, including ReliefF, minimum correlation, Chi-square test, and others, to identify the most relevant predictors for PV output prediction. Two predictive models, the multilayer perceptron (MLP) and long short-term memory (LSTM) networks, are developed and tested on a real-world dataset from southern Algeria. The results demonstrate that applying feature selection significantly improves forecasting accuracy. For instance, integrating ReliefF with MLP reduced the normalized mean absolute error (nMAE) to 9.21% with an R

Indexed as

Artificial neural networksFeatures selectionForecastingPV powerRenewable energy

Identifiers

PMID40595136
PMCPMC12215858

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.