PVInsight Phase 2

Access to increasing volume of photovolatic (PV) system performance data creates opportunities for monitoring system health and optimizing operations and maintenance (O&M) activities. Analyzing production data from installed PV systems allows for non-intrusive, remote, and automated assessment of performance issues. Doing this at scale will allow us to ensure that the large volume of distributed, rooftop PV systems being installed have good reliability and that they constitute a dependable grid resource, not a destabilizing burden for grid operators. However, standard approaches to analyzing PV performance require:

• A significant amount of engineering time 
• Knowledge of PV system modeling science and best practices
• Accurate system configuration information
• Access to reliable irradiance and meteorological data 

While these requirements are typically met for large, utility-scale PV systems, distributed, rooftop systems do not generally meet these requirements, and are therefore being mostly ignored by digital O&M companies, to the detriment of these systems and the loss of value to their owners.

We seek to develop algorithms to automate loss factor estimations and performance analysis for small and medium-sized PV systems. Drawing from the disciplines of optimization, signal processing, and machine learning, we are developing novel solutions to difficult data problems and implementing these solutions in an open-source software toolkit, written in Python. These tools will allow users to process data from hundreds of thousands of unique PV systems, automatically detecting operational issues and degradation patterns and forecasting system power production.

Project lead: Bennet Meyers