Solar Radiation Prediction by using ML

Project Code :TCMAPY1057

Objective

The objective of this study is to develop and evaluate machine learning models for accurate prediction of solar radiation levels, enabling efficient utilization of solar energy resources and supporting the integration of renewable energy sources into the energy grid.

Abstract

Solar energy is a crucial and sustainable source of renewable energy with growing importance in mitigating climate change and addressing energy demands. Accurate prediction of solar radiation is essential for optimizing the efficiency of solar energy systems. This study presents a comprehensive overview of machine learning approaches for solar radiation prediction. We explore various data sources, including weather data, satellite imagery, and historical solar radiation records, and apply machine learning algorithms to predict solar radiation levels with high precision. The models discussed in this research are evaluated using real-world data and performance metrics, highlighting their potential in aiding solar energy planning and management. This work contributes to the advancement of reliable solar radiation prediction techniques, promoting the effective utilization of solar energy resources and fostering a greener and more sustainable future.

KEYWORDS: Radiation, Temperature, Humidity, Speed.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.