E-ISSN 2414-6013 | ISSN 2411-9636
 
2026, Vol: 15, Issue: 1
15 / 1Current Issue ArchiveAims and ScopeAbstracting & IndexingMost Accessed ArticlesMost Downloaded ArticlesMost Cited Articles

Technical Note
Online Published: 13 Apr 2025
 


Optimizing Solar Radiation Forecasting for Renewable Energy Systems: : A Comparative Analysis of Machine Learning and Feature Engineering Techniques

Ajeeb Sagar, Shreyas Hole, Vinoth Kolluru.


Abstract
Accurate solar radiation prediction is pivotal for optimizing solar energy systems, as it allows for better energy storage, grid integration, and renewable energy planning. This study compares the predictive accuracy of three machine learning models—Random Forest, XGBoost, and Multilayer Perceptron (MLP)- in forecasting solar radiation based on a meteorological and temporal features dataset. The dataset, encompassing Temperature, humidity, wind speed, and sunrise/sunset times, was preprocessed through transformations (Box-Cox, logarithmic scaling) and feature selection methods (SelectKBest, Extra Trees Classifier) to enhance model performance. XGBoost demonstrated superior performance, achieving an R² of 0.93 and RMSE of 81.87, effectively capturing complex nonlinear relationships within the data. MLP, while slightly lower in R², yielded the lowest mean absolute error (MAE = 41.74), underscoring its precision in individual predictions. SelectKBest identified set Hour (sunset hour), Month, and Wind Direction as critical features, while Extra Trees prioritized Wind Direction, Minute, and Humidity, reflecting model-specific feature importance. Collectively, these models illustrate the benefits of integrating feature engineering with advanced machine learning for renewable energy optimization, with XGBoost and MLP demonstrating particular efficacy for accurate solar radiation forecasting. This study underscores the potential of machine learning in enhancing solar energy management, facilitating a more efficient transition to sustainable energy sources.

Key words: Random Forest, XGBoost, MLP, Solar radiation, renewable energy optimization, solar energy forecasting, temporal features, meteorological data, solar energy systems.


 
ARTICLE TOOLS
Abstract
PDF Fulltext
How to cite this articleHow to cite this article
Citation Tools
Related Records
 Articles by Ajeeb Sagar
Articles by Shreyas Hole
Articles by Vinoth Kolluru
on Google
on Google Scholar


How to Cite this Article
Pubmed Style

Sagar A, Hole S, Kolluru V. Optimizing Solar Radiation Forecasting for Renewable Energy Systems: : A Comparative Analysis of Machine Learning and Feature Engineering Techniques. Sol. Energy Sustain. Dev.. 2025; 14(1): 295-315.

doi:10.51646/jsesd.v14i1.386

Web Style

Sagar A, Hole S, Kolluru V. Optimizing Solar Radiation Forecasting for Renewable Energy Systems: : A Comparative Analysis of Machine Learning and Feature Engineering Techniques. https://www.jsesd.org/?mno=302658348 [Access: November 12, 2026].

doi:10.51646/jsesd.v14i1.386

AMA (American Medical Association) Style

Sagar A, Hole S, Kolluru V. Optimizing Solar Radiation Forecasting for Renewable Energy Systems: : A Comparative Analysis of Machine Learning and Feature Engineering Techniques. Sol. Energy Sustain. Dev.. 2025; 14(1): 295-315.


doi:10.51646/jsesd.v14i1.386

Vancouver/ICMJE Style

Sagar A, Hole S, Kolluru V. Optimizing Solar Radiation Forecasting for Renewable Energy Systems: : A Comparative Analysis of Machine Learning and Feature Engineering Techniques. Sol. Energy Sustain. Dev.. (2025), [cited November 12, 2026]; 14(1): 295-315.


doi:10.51646/jsesd.v14i1.386

Harvard Style

Sagar, A., Hole, . S. & Kolluru, . V. (2025) Optimizing Solar Radiation Forecasting for Renewable Energy Systems: : A Comparative Analysis of Machine Learning and Feature Engineering Techniques. Sol. Energy Sustain. Dev., 14 (1), 295-315.


doi:10.51646/jsesd.v14i1.386

Turabian Style

Sagar, Ajeeb, Shreyas Hole, and Vinoth Kolluru. 2025. Optimizing Solar Radiation Forecasting for Renewable Energy Systems: : A Comparative Analysis of Machine Learning and Feature Engineering Techniques. Solar Energy and Sustainable Development, 14 (1), 295-315.


doi:10.51646/jsesd.v14i1.386

Chicago Style

Sagar, Ajeeb, Shreyas Hole, and Vinoth Kolluru. "Optimizing Solar Radiation Forecasting for Renewable Energy Systems: : A Comparative Analysis of Machine Learning and Feature Engineering Techniques." Solar Energy and Sustainable Development 14 (2025), 295-315.


doi:10.51646/jsesd.v14i1.386

MLA (The Modern Language Association) Style

Sagar, Ajeeb, Shreyas Hole, and Vinoth Kolluru. "Optimizing Solar Radiation Forecasting for Renewable Energy Systems: : A Comparative Analysis of Machine Learning and Feature Engineering Techniques." Solar Energy and Sustainable Development 14.1 (2025), 295-315. Print.


doi:10.51646/jsesd.v14i1.386

APA (American Psychological Association) Style

Sagar, A., Hole, . S. & Kolluru, . V. (2025) Optimizing Solar Radiation Forecasting for Renewable Energy Systems: : A Comparative Analysis of Machine Learning and Feature Engineering Techniques. Solar Energy and Sustainable Development, 14 (1), 295-315.


doi:10.51646/jsesd.v14i1.386

Author LoginReviewer LoginAbout PublisherAI Policy

About Solar Energy and Sustainable Development

About the JournalThe Journal of Solar Energy and Sustainable Development (JSESD) is a peer-reviewed, open-access, biannual scientific journal dedicated to ... Read more.



For best results, please use Internet Explorer or Google Chrome.

Contact Information