Diurnal Changes and Machine Learning Analysis of Perovskite Modules Based on Two Years of Outdoor Monitoring

Long-term stability is the primary challenge for the commercialization of perovskite photovoltaics, exacerbated by limited outdoor data and unclear correlations between indoor and outdoor tests. In this study, we report on the outdoor stability testing of perovskite mini-modules conducted over a two-year period. We conducted a detailed analysis of the changes in performance across the day, quantifying both the diurnal degradation and the overnight recovery. Additionally, we employed the XGBoost regression model to forecast the power output. Our statistical analysis of extensive aging data showed that all perovskite configurations tested exhibited diurnal degradation and recovery, maintaining a linear relationship between these phases across all environmental conditions. Our predictive model, focusing on essential environmental parameters, accurately forecasted the power output of mini-modules with a 6.76% nRMSE, indicating its potential to predict the lifetime of perovskite-based devices.

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Publication Details

Journal
ACS Energy Letters
Published
2024-09-26
DOI
https://doi.org/10.1021/acsenergylett.4c01943
Citations
14
Primary Topic
Perovskite Materials and Applications
Type
article
Field-Weighted Citation Impact
2.92

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article

Diurnal Changes and Machine Learning Analysis of Perovskite Modules Based on Two Years of Outdoor Monitoring

Anurag Krishna, Tom Aernouts, Aránzazu Aguirre, Tamara Merckx et al.
14 citations
ACS Energy Letters
Perovskite Materials and Applications
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article

Diurnal Changes and Machine Learning Analysis of Perovskite Modules Based on Two Years of Outdoor Monitoring

Anurag Krishna, Tom Aernouts, Aránzazu Aguirre, Tamara Merckx, Andreas Kyprianou, George E. Georghiou, Santhosh Ramesh, Matthew Norton, Maria Hadjipanayi, Elias Peraticos, Vasiliki Paraskeva, Andreas Livera, Rita Ebner
article en
14 citations

Abstract

Long-term stability is the primary challenge for the commercialization of perovskite photovoltaics, exacerbated by limited outdoor data and unclear correlations between indoor and outdoor tests. In this study, we report on the outdoor stability testing of perovskite mini-modules conducted over a two-year period. We conducted a detailed analysis of the changes in performance across the day, quantifying both the diurnal degradation and the overnight recovery. Additionally, we employed the XGBoost regression model to forecast the power output. Our statistical analysis of extensive aging data showed that all perovskite configurations tested exhibited diurnal degradation and recovery, maintaining a linear relationship between these phases across all environmental conditions. Our predictive model, focusing on essential environmental parameters, accurately forecasted the power output of mini-modules with a 6.76% nRMSE, indicating its potential to predict the lifetime of perovskite-based devices.

ACS Energy LettersVol. 9(10)
AIT Austrian Institute of Technology GmbH (AT), University of Cyprus (CY), IMEC (BE), Hasselt University (BE)
European Commission, European Regional Development Fund, HORIZON EUROPE Widening participation and spreading excellence
Openalex Percentile: Top 8%
Perovskite Materials and Applications
2.92
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