The Robust PV Performance Loss Rate Determination and Power Forecasting project through the Solar Durability and Lifetime Extension (SDLE) Research Center involves a number of members and collaborators. Learn more about the project below. 

Project Overview

A recent trend in PV research is to study and learn about PV system performance based on real-world datastreams of distributed and utility scale PV systems, instead of only relying on lab-based experimentation. Two major PV research challenges are to determine and control the performance loss rate (PLR) of PV systems over their lifetimes, and to forecast their power output up to six hours or one to five days.

A novel class of adaptive Graph Neural Network (GNN) models will enable us to devise and develop a spatiotemporal-based learning framework for PV power plants that addresses these two challenges while simultaneously enriching the real-world datastreams automatically, using underutilized data sources for data imputation. The GNN models will automatically curate spatiotemporal weather, irradiance, and power data from multiple PV systems and data sources into a system topology-aware network. 

The GNN will determine PLR for specific systems and module brands and forecast PV power output for specific systems and regions. In addition to addressing PLR and power forecasting, the GNN model will be able to diagnose localization of anomalies, such as hurricane or tornado tracts, and reliably evaluate robustness to malicious data manipulation that can occur in cyber attacks.

Members and Collaborators

• Roger H. French (Principal Investigator and Kyocera Professor, CWRU)
• Laura S. Bruckman (Research Associate Professor, CWRU)
• Yinghui Wu (Assistant Professor, CWRU and Staff Scientist, Pacific Northwest National Laboratory)
• Mehmet Koyutürk (Andrew R. Jennings Professor of Computing Sciences, Electrical Engineering & Computer Science, CWRU)
• Jennifer L. Braid (Visiting Researcher, Sandia National Laboratories)
• Jean-Nicolas Jaubert (Director, Product Reliability, Certification & System Performance, CanadianSolar)
• David H. Meakin (Senior Manager Module Reliability, SunPower)
• Mohamed Arafath Nihar (Department of Computer and Data Sciences, CWRU)
• Alan Curran (Department of Materials Science and Engineering, CWRU)
• Nick Steffan
• Benjamin Spurgeon

Unfunded Team Members

• SunPower
• Brookfield Renewables
• C2 Energy
• SolarEdge
• CanadianSolar

Acknowledgements

This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE0009353.