Machine Learning-based Classification of Spectral Conditions for High-Throughput Indoor Testing of Photovoltaic Modules

Looney E, Haohui L, Ren Z, Buonassisi T, Peters IM (2019)

Publication Type: Conference contribution

Publication year: 2019

Journal

Conference Record of the IEEE Photovoltaic Specialists Conference Institute of Electrical and Electronics Engineers

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2

Pages Range: 3562-3566

Conference Proceedings Title: Conference Record of the IEEE Photovoltaic Specialists Conference

Event location: Chicago, IL, USA

ISBN: 9781728104942

DOI: 10.1109/PVSC40753.2019.9198982

Abstract

High-throughput testing of solar modules to accurately predict energy yield (EY) is increasingly important as more of the power grid runs on photovoltaics (PV). Modules are sold based on power ratings measured under standard testing conditions, not fully considering environmental conditions of the real world. In this work, we use the k-means algorithm to extract the best representative conditions of the environment that minimizes error in EY. The work presented here is a fully scoped proof-of-concept demonstrated on a year of spectral data clustered and analyzed for every month of 2017 in Boulder, Colorado. Preliminary results demonstrate a decrease in 5 percent relative error inenergy yield predictions between one standard testing condition and up to seven clusters found withthis method. This can be generalized to more locations around the world as a powerful tool for EY estimation. These results demonstrate the capacity for high throughput, accurate EY prediction usingclustered conditions.

Involved external institutions

Massachusetts Institute of Technology (MIT) US United States (USA) (US) National University of Singapore (NUS) SG Singapore (SG)

How to cite

APA:

Looney, E., Haohui, L., Ren, Z., Buonassisi, T., & Peters, I.M. (2019). Machine Learning-based Classification of Spectral Conditions for High-Throughput Indoor Testing of Photovoltaic Modules. In Conference Record of the IEEE Photovoltaic Specialists Conference (pp. 3562-3566). Chicago, IL, USA: Institute of Electrical and Electronics Engineers Inc..

MLA:

Looney, Erin, et al. "Machine Learning-based Classification of Spectral Conditions for High-Throughput Indoor Testing of Photovoltaic Modules." Proceedings of the 46th IEEE Photovoltaic Specialists Conference, PVSC 2019, Chicago, IL, USA Institute of Electrical and Electronics Engineers Inc., 2019. 3562-3566.

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