Abstract: Solar energy is becoming the most used renewable energy in the world. Because of its environmental benefits and sustainability. Solar energy gives the most radiation due to recent weather conditions. We should overcome this challenging task. This project aims to predict the solar radiation intensity of the future by researching past historical weather conditions and time series data using machine learning techniques. The project proposed system uses the parameters like temperature, humidity, wind speed, atmospheric pressure and cloud as input features for the prediction models. Machine learning algorithms including long short term memory and support vector regression are applied to the complex and non-linear relationships between weather conditions and no solar radiance. Time series analysis tools are employed to capture only the seasonal trends and temporal dependencies present in the data. Accurate prediction of solar energy is essential for proper planning and working of revenue energy systems. This system project focuses on scanning solar radiation intensity using time series data and machine learning techniques. Time series forecasting models like auto regressive integrated moving average (ARIMA) are implemented to capture the seasonal variations present in the data. The proposed system predicted solar radiation values can be used to improve energy generation, solar panel deployment and support smart grid operations. By providing the accurate solar irradiance forecast that the system helps to reduce uncertainty in solar power generation. This system enhances the reliability of renewable energy systems. This system shows the result of machine learning based traditional prediction and demonstrating their effectiveness in solar radiation forecasting and renewable energy management.

Index Terms: Solar Radiation Prediction, Solar irradiance forecasting, ARIMA, Time series analysis.

Downloads: | DOI: 10.17148/IARJSET.2026.13416