• sudhir singh
• Dr. nagendra swarnkar

The integration of a large proportion of renewable energy reduces the reliability and stability of a power system. Renewable energy sources such as solar and wind are highly non predictive, making it difficult to maintain system stability with an unacceptably high proportion of renewable energy input. Solar power forecasting can be used to improve system stability by providing estimated future power generation to system control engineers. Machine Learning (ML) algorithms have demonstrated excellent results in time series forecasting and can thus be used to forecast power using weather conditions as model inputs. To forecast solar power, multiple machine learning, deep learning, and artificial neural network techniques are used. In this regression model, machine learning techniques such as support vector machine regression, random forest regression, and linear regression model were used, with the random forest regression outperforming the other two regression models in terms of accuracy.

Solar Radiation, Power, solar power forecasting, renewable energy, solar power in Sri Lanka, machine learning for forecasting

"Study the prediction of Solar Radiation through Machine learning Algorithm in different weather condition to Achieve Maximum Power", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.k433-k442, May-2025, Available :http://www.jetir.org/papers/JETIR2505B32.pdf

"Study the prediction of Solar Radiation through Machine learning Algorithm in different weather condition to Achieve Maximum Power", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppk433-k442, May-2025, Available at : http://www.jetir.org/papers/JETIR2505B32.pdf