ArticleScientific reports2025
Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems.
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 5 papers.
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Who cites it
5 citing papers in PubMed.
- Comparing machine learning and deep learning approaches to predicting the seismic response of slab-column connections.Scientific reports · 2026Article
- Accurate forecasting of photovoltaic optimal points and efficiency using advanced hybrid machine learning models.Scientific reports · 2026Article
- PhysEmbedFormer: a physics-guided interpretable architecture for days-ahead forecasting of PV power.Scientific reports · 2026Article
- Adaptive Control-based frequency control strategy for PV/ DEG/ battery power system during islanding conditions.Scientific reports · 2025Article
- Advanced deep learning modeling to enhance detection of defective photovoltaic cells in electroluminescence images.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents a machine learning (ML) model designed to track the maximum power point of standalone Photovoltaic (PV) systems. Due to the nonlinear nature of power generation in PV systems, influenced by fluctuating weather conditions, managing this nonlinear data effectively remains a challenge. As a result, the use of ML techniques to optimize PV systems at their MPP is highly beneficial. To achieve this, the research explores various ML algorithms, such as Linear Regression (LR), Ridge Regression (RR), Lasso Regression (Lasso R), Bayesian Regression (BR), Decision Tree Regression (DTR), Gradient Boosting Regression (GBR), and Artificial Neural Networks (ANN), to predict the MPP of PV systems. The model utilizes data from the PV unit's technical specifications, allowing the algorithms to forecast maximum power, current, and voltage based on given irradiance and temperature inputs. Predicted data is also used to determine the boost converter's duty cycle. The simulation was conducted on a 100 kW solar panel with an open-circuit voltage of 64.2 V and a short-circuit current of 5.96 A. Model performance was evaluated using metrics such as Root Mean Square Error (RMSE), Coefficient of Determination (R
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Registered trials
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 Socratic graph.