• YERAMOLU VENKATA DURGA DEVI.
• PADALA SREENIVASA REDDY

As a result of the climate and energy crises, renewable energy generation, including solar generation, has grown significantly. In smart grid, photovoltaic (PV) generation is becoming increasingly prevalent. Because the solar source at ground level is highly dependent on cloud cover variability, atmospheric aerosol levels, and other atmosphere parameters, solar power is intermittent and variable. Large-scale solar generation's inherent variability poses significant challenges to smart grid energy management. Accurate forecasting of solar power/irradiance is critical to ensuring the smart grid's economic operation. We employ a number of variable selection techniques, which lead us to believe that air temperature, relative humidity, and dew point are the most informative weather parameters for predicting solar energy generation at the power plant. A tensor flow-based

SOLAR ENERGY FORECASTING USING MACHINE LEARNING

"SOLAR ENERGY FORECASTING USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 8, page no.d437-d442, August-2021, Available :http://www.jetir.org/papers/JETIR2108426.pdf

"SOLAR ENERGY FORECASTING USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 8, page no. ppd437-d442, August-2021, Available at : http://www.jetir.org/papers/JETIR2108426.pdf