Abstract :

Solar Energy Is One Of The Most Important Renewable Energy Sources Used To Produce Electricity. However, The Amount Of Solar Power Generated Varies Depending On Weather Conditions Such As Sunlight Intensity, Temperature, Cloud Cover, And Humidity. Accurate Prediction Of Solar Power Generation Helps In Efficient Energy Management, Grid Stability, And Better Utilization Of Renewable Resources. This Project Focuses On Developing A Solar Power Generation Prediction System Using Historical Weather And Solar Data. Machine Learning Techniques Are Applied To Analyze The Relationship Between Environmental Parameters And Solar Energy Output. The System Collects And Processes Data Such As Solar Irradiance, Temperature, Wind Speed, And Humidity To Predict Future Power Generation. The Proposed Model Improves Prediction Accuracy And Helps Power Plants And Energy Providers Plan Electricity Distribution Effectively. By Forecasting Solar Power Generation In Advance, The System Supports Better Decision-making, Reduces Energy Wastage, And Promotes The Efficient Use Of Renewable Energy Resources

Published:

06-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1338-1345

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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