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Title: :  SOLAR RADIATION PREDICTION USING MACHINE LEARNING
PaperId: :  26146
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 2 2025
DUI:    16.0415/IJARIIE-26146
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
M RAM KUMARSiddharth Institute of Engineering & Technology (SIETK)
ANDE NAVITHASiddharth Institute of Engineering & Technology (SIETK)
M M JAGANSiddharth Institute of Engineering & Technology (SIETK)
MATTIGALLA NANDHA KUMARSiddharth Institute of Engineering & Technology (SIETK)
AYYAPPARAJU RENUSREESiddharth Institute of Engineering & Technology (SIETK)
DONAPATI VAMSHIDHARA REDDYSiddharth Institute of Engineering & Technology (SIETK)

Abstract

Information Technology
Decision Tree, Random Forest, AdaBoost, Linear Regression, KNN, SVR
Solar energy is an abundant and sustainable source of power, making it a critical component of the global transition towards clean energy solutions. Accurate prediction of solar radiation is essential for optimizing the performance of solar energy systems, such as photovoltaic panels and solar thermal plants. This study presents a comprehensive exploration of machilearning techniques for the prediction of solar radiation.Machine learning models have been developed and trained on historical solar radiation data, incorporating various meteorological parameters, geographical factors, and time-related features. The predictive accuracy of these models has been evaluated using real-world datasets from diverse geographic locations and climates.The results demonstrate the effectiveness of machine learning algorithms in accurately forecasting solar radiation levels. These predictions can empower energy stakeholders, grid operators, and solar energy system operators to make informed decisions regarding energy generation, distribution, and consumption. Additionally, the study highlights the significance of feature engineering, model selection, and hyper parameter tuning in enhancing prediction performance.The methodology involves data preprocessing, feature engineering, model selection, and evaluation using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Additionally, model interpretability techniques, such as feature importance analysis, will be employed to enhance the transparency of predictions.

Citations

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IJARIIE M RAM KUMAR, ANDE NAVITHA, M M JAGAN, MATTIGALLA NANDHA KUMAR, AYYAPPARAJU RENUSREE, and DONAPATI VAMSHIDHARA REDDY. "SOLAR RADIATION PREDICTION USING MACHINE LEARNING" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 2 2025 Page 1448-1456
MLA M RAM KUMAR, ANDE NAVITHA, M M JAGAN, MATTIGALLA NANDHA KUMAR, AYYAPPARAJU RENUSREE, and DONAPATI VAMSHIDHARA REDDY. "SOLAR RADIATION PREDICTION USING MACHINE LEARNING." International Journal Of Advance Research And Innovative Ideas In Education 11.2(2025) : 1448-1456.
APA M RAM KUMAR, ANDE NAVITHA, M M JAGAN, MATTIGALLA NANDHA KUMAR, AYYAPPARAJU RENUSREE, & DONAPATI VAMSHIDHARA REDDY. (2025). SOLAR RADIATION PREDICTION USING MACHINE LEARNING. International Journal Of Advance Research And Innovative Ideas In Education, 11(2), 1448-1456.
Chicago M RAM KUMAR, ANDE NAVITHA, M M JAGAN, MATTIGALLA NANDHA KUMAR, AYYAPPARAJU RENUSREE, and DONAPATI VAMSHIDHARA REDDY. "SOLAR RADIATION PREDICTION USING MACHINE LEARNING." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 2 (2025) : 1448-1456.
Oxford M RAM KUMAR, ANDE NAVITHA, M M JAGAN, MATTIGALLA NANDHA KUMAR, AYYAPPARAJU RENUSREE, and DONAPATI VAMSHIDHARA REDDY. 'SOLAR RADIATION PREDICTION USING MACHINE LEARNING', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 2, 2025, p. 1448-1456. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/SOLAR_RADIATION_PREDICTION_USING_MACHINE_LEARNING_ijariie26146.pdf (Accessed : ).
Harvard M RAM KUMAR, ANDE NAVITHA, M M JAGAN, MATTIGALLA NANDHA KUMAR, AYYAPPARAJU RENUSREE, and DONAPATI VAMSHIDHARA REDDY. (2025) 'SOLAR RADIATION PREDICTION USING MACHINE LEARNING', International Journal Of Advance Research And Innovative Ideas In Education, 11(2), pp. 1448-1456IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/SOLAR_RADIATION_PREDICTION_USING_MACHINE_LEARNING_ijariie26146.pdf (Accessed : )
IEEE M RAM KUMAR, ANDE NAVITHA, M M JAGAN, MATTIGALLA NANDHA KUMAR, AYYAPPARAJU RENUSREE, and DONAPATI VAMSHIDHARA REDDY, "SOLAR RADIATION PREDICTION USING MACHINE LEARNING," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 2, pp. 1448-1456, Mar-App 2025. [Online]. Available: https://ijariie.com/AdminUploadPdf/SOLAR_RADIATION_PREDICTION_USING_MACHINE_LEARNING_ijariie26146.pdf [Accessed : ].
Turabian M RAM KUMAR, ANDE NAVITHA, M M JAGAN, MATTIGALLA NANDHA KUMAR, AYYAPPARAJU RENUSREE, and DONAPATI VAMSHIDHARA REDDY. "SOLAR RADIATION PREDICTION USING MACHINE LEARNING." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 2 ().
Vancouver M RAM KUMAR, ANDE NAVITHA, M M JAGAN, MATTIGALLA NANDHA KUMAR, AYYAPPARAJU RENUSREE, and DONAPATI VAMSHIDHARA REDDY. SOLAR RADIATION PREDICTION USING MACHINE LEARNING. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : ]; 11(2) : 1448-1456. Available from: https://ijariie.com/AdminUploadPdf/SOLAR_RADIATION_PREDICTION_USING_MACHINE_LEARNING_ijariie26146.pdf
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