• Shradha Jadhav
• A.S.Deokate
• Aishwarya Melgiri
• Somesh Kamble
• S.S.Savkare

the system was able to detect all faults if studied over a two-week period. These findings show that a machine-learning model may be used to simulate the projected output of a solar panel system (using meteorological data) and use it to determine if the system is producing as much power as it should be. Adding meteorological the rising use of solar panels around the world emphasizes the need of being able to detect defects in solar-panel-based systems. In this study, historical solar panel power output (kWh) was integrated with meteorological data to build a machine-learning model that could predict the projected power production of a solar panel system. A comparison of the expected and actual power outputs was done using the expected power output to determine if the system was faulty. As a result, while simulating a failure (a 50% reduction data, enhancing the precision of the meteorological data, and training the machine learning model on more data are some of the alternatives for improving the system.

Python, Sckit-learn.

"Quality Analysis of Solar Panel", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.590-594, August-2022, Available :http://www.jetir.org/papers/JETIRFP06105.pdf

"Quality Analysis of Solar Panel", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. pp590-594, August-2022, Available at : http://www.jetir.org/papers/JETIRFP06105.pdf