ArticleScientific reports2024
Employing machine learning for advanced gap imputation in solar power generation databases.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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Who cites it
3 citing papers in PubMed.
- Hydrophobic wavy tape to augment the parabolic trough solar collector performance.Scientific reports · 2025Article
- Dynamic Model Selection in a Hybrid Ensemble Framework for Robust Photovoltaic Power Forecasting.Sensors (Basel, Switzerland) · 2025Article
- Comparative analysis of daily global solar radiation prediction using deep learning models inputted with stochastic variables.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
This research evaluates the application of advanced machine learning algorithms, specifically Random Forest and Gradient Boosting, for the imputation of missing data in solar energy generation databases and their impact on the size of green hydrogen production systems. The study demonstrates that the Random Forest model notably excels in harnessing solar data to optimize hydrogen production, achieving superior prediction accuracy with mean absolute error (MAE) of 0.0364, mean squared error (MSE) of 0.0097, root mean squared error (RMSE) of 0.0985, and a coefficient of determination (R
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Registered trials
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