Parveen Bhola, a research scholar at India's Thapar Institute of Engineering and Technology, and Saurabh Bhardwaj, an associate professor at the same institution, spent the last few years developing and improving statistical and machine learning-based alternatives to enable real-time inspection of solar panels.

Their research found a new application for clustering-based computation, which uses past meteorological data to compute performance ratios and degradation rates.

Clustering-based computation is advantageous for this problem because of its ability to speed up the inspection process, preventing further damage and hastening repairs, by using a performance ratio based on meteorological parameters that include temperature, pressure, wind speed, humidity, sunshine hours, solar power, and even the day of the year.

The parameters are easily acquired and assessed, and can be measured from remote locations.

Improving PV cell inspection systems could help inspectors troubleshoot more efficiently and potentially forecast and control for future difficulties.

Clustering-based computation is likely to shed light on new ways to manage solar energy systems, optimizing PV yields, and inspiring future technological advancements in the field.

The text above is a summary, you can read full article here.