Artificial Intelligence-Based, Wavelet-Aided Prediction of Long-Term Outdoor Performance of Perovskite Solar Cells

Ioannis Kouroudis, Kenedy Tabah Tanko, Masoud Karimipour, Aziz Ben Ali, D. Kishore Kumar, Vediappan Sudhakar, Ritesh Kant Gupta, Iris Visoly-Fisher, Monica Lira-Cantu, Alessio Gagliardi

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The commercial development of perovskite solar cells (PSCs) has been significantly delayed by the constraint of performing time-consuming degradation studies under real outdoor conditions. These are necessary steps to determine the device lifetime, an area where PSCs traditionally suffer. In this work, we demonstrate that the outdoor degradation behavior of PSCs can be predicted by employing accelerated indoor stability analyses. The prediction was possible using a swift and accurate pipeline of machine learning algorithms and mathematical decompositions. By training the algorithms with different indoor stability data sets, we can determine the most relevant stress factors, thereby shedding light on the outdoor degradation pathways. Our methodology is not specific to PSCs and can be extended to other PV technologies where degradation and its mechanisms are crucial elements of their widespread adoption.

Original languageEnglish
Pages (from-to)1581-1586
Number of pages6
JournalACS Energy Letters
Volume9
Issue number4
DOIs
StatePublished - 12 Apr 2024

ASJC Scopus subject areas

  • Chemistry (miscellaneous)
  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Materials Chemistry

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