Evidence map›Paper›PMID 42447107›Full record

ArticlePloS one2026

Machine learning-based long-term degradation and LCOE Analysis of floating PV with custom pontoon design.

Roby Mohajon, Md Rabiul Islam Polash, Md Nabil Shahriar, Anupom Bhowmick, Pranab Kumar Mondol, Hrittik Mutsuddi, Jihanul Haque Jihan, Nur Mohammad

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Roby MohajonDepartment of Electrical and Electronic Engineering, Barishal Engineering College, University of Dhaka, North Durgapur, Barishal, Bangladesh.ORCID https://orcid.org/0009-0009-3117-1842
Md Rabiul Islam PolashDepartment of Electrical and Electronic Engineering, Barishal Engineering College, University of Dhaka, North Durgapur, Barishal, Bangladesh.
Md Nabil ShahriarDepartment of Electrical and Electronic Engineering, Barishal Engineering College, University of Dhaka, North Durgapur, Barishal, Bangladesh.
Anupom BhowmickDepartment of Electrical and Electronic Engineering, Barishal Engineering College, University of Dhaka, North Durgapur, Barishal, Bangladesh.
Pranab Kumar MondolDepartment of Electrical and Electronic Engineering, Barishal Engineering College, University of Dhaka, North Durgapur, Barishal, Bangladesh.
Hrittik MutsuddiDepartment of Electrical and Electronic Engineering, Barishal Engineering College, University of Dhaka, North Durgapur, Barishal, Bangladesh.ORCID https://orcid.org/0009-0000-2301-9482
Jihanul Haque JihanDepartment of Electrical and Electronic Engineering, Barishal Engineering College, University of Dhaka, North Durgapur, Barishal, Bangladesh.
Nur MohammadDepartment of Electrical and Electronic Engineering, Chittagong University of Engineering and Technology, Chittagong, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Floating Photovoltaic (FPV) systems are a feasible alternative for solar energy utilization in areas where land is scarce. However, issues related to performance degradation, ecological effects, and economic viability have restricted the widespread acceptance of this technology. This study examines the eco-compatibility of a 5 MW FPV solar plant, which is suitable for a wetland ecosystem in Bangladesh, and examines the technical feasibility, environmental sustainability, and economic viability of the plant. In this study, a model was developed using detailed system-level simulations, and the model validated the structural feasibility of a specially designed light-permeable annular pontoon by performing hydrostatic buoyancy and stability tests. Ecological compatibility is assessed using a light-transmission-based photosynthetic viability model, whereas the long-term degradation of the performance ratio and energy yield over a 25-year lifetime is predicted using a climate-aware machine learning framework that incorporates irradiance, temperature, humidity, and system aging effects. Furthermore, an economic model sensitive to inflation was used to examine the levelized cost of electricity (LCOE) in the case study. The results show an average performance ratio of 82.4%, lifecycle energy outputs of approximately 222 GWh, and an LCOE of 0.0315 USD/kWh. In addition, approximately 70% of the aquatic photosynthesis potential is retained, and approximately 104,149.5 tCO₂ is eliminated.

Indexed as

Machine LearningSolar EnergyBangladeshElectricityPhotosynthesisWetlands

Identifiers

PMID42447107
PMCPMC13367708

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Registered trials

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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.