Evidence map›Paper›PMID 41249226›Full record

ArticleScientific reports2025

Optimized energy management of PV-Powered lighting system for smart cities using perfumer optimization algorithm and graph ensemble neural network.

Zakir Hussain, Prabu Selvam, M Sivaramkrishnan, M Siva Ramkumar, Sivaprakash Paramasivam, Mohamed Ghouse, T M Yunus Khan, Md Amir Khan, Amanuel Zewdie

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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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. 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.

Zakir HussainDepartment of Chemical Technology, Loyola Academy, Secunderabad, 500015, Telangana, India.
Prabu SelvamSchool of Computing, SRM Institute of Science & Technology, Tiruchirappalli, 621105, Tamil Nadu, India.
M SivaramkrishnanDepartment of Electrical and Electronics Engineering, Karpagam College of Engineering, Coimbatore, 641032, Tamil Nadu, India. krishbe95@gmail.com.
M Siva RamkumarDepartment of Electronics and communication Engineering, SNS College of Technology, Coimbatore, Tamil Nadu, India.
Sivaprakash ParamasivamDepartment of Mechatronics Engineering, Agni College of Technology, OMR, Chennai, 600130, India.
Mohamed GhouseDepartment of Computer Science, College of Computer Science, King Khalid University, Alfaraa, Abha, Saudi Arabia.
T M Yunus KhanDepartment of Mechanical Engineering, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia.
Md Amir KhanAl-Ayen Scientific Research Center, Al-Ayen Iraqi University, AUIQ, An Nasiriyah, P.O. Box: 64004, Thi Qar, Iraq.
Amanuel ZewdieSchool of Informatics and Computer Science, Dilla University, Po. Box 419, Dilla, Ethiopia. amanuelz@du.edu.et.

Funding

King Khalid University RGP2/487/46
6 · The paper itself

Abstract

Energy Management of PV-powered lighting systems for smart cities integrates RES, such as PV panels, WT, battery ESS, and the grid to optimize energy utilization, enhance sustainability, and reduce reliance on conventional energy sources. At the same time, the system has to address problems in balancing costs and efficiency, because the rise in RE like PV and wind, and the need for ESS and grid integration, can make it harder to match supply and demand. To address these issues, this paper proposes a hybrid strategy for EM in PV-powered lighting systems for smart cities. The hybrid method integrates the POA and GENN. The main aim is to reduce operational costs and improve the energy efficiency of the PV-powered lighting system. POA is utilized to optimize energy allocation among RES, the grid, and energy storage, enhancing resource utilization and maintaining supply-demand balance. While GENN is utilized to predict energy generation and consumption patterns, it enhances the accuracy of forecasting for improved system performance. By then, the proposed method is implemented on the MATLAB platform and evaluated with various existing approaches. The POA-GENN approach achieves an operational cost of 365.24 €ct and an efficiency of 99.2%, demonstrating its effectiveness in optimizing the EM of PV-powered lighting systems for smart cities.

Indexed as

And wind turbineEnergy management controllerEnergy management systemGridPhotovoltaicSmart homeSmart meter

Identifiers

PMID41249226
PMCPMC12624081

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

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.