Applying unsupervised machine learning for the detection of shading on a portfolio of commercial roof-top power plants in Germany

Obstacles that cast shading on commercially oper-ated PV power plants can lead to a variety of issues, besides causing less energy, like false alerts in failure detection systems or skewed performace ratios. The detection and monitoring of shading effects using on-site inspections can be challenging, especially when one handles a large portfolio of power plants over a period of many years, since shading behaviours can also change over time. We apply an unsupervised method for detecting shading directly from power measurements to create so called shading masks, which make binary statements over whether or not a power plant or subplant is subject to shading at a given time. The shading masks are compared with the results of on-site inspections and they are used to create loss estimates.