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

IF 19.3 1区材料科学 Q1 CHEMISTRY, PHYSICAL

ACS Energy Letters Pub Date : 2024-09-26 DOI:10.1021/acsenergylett.4c01943

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

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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.

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基于两年户外监测的 Perovskite 模块昼夜变化和机器学习分析

由于室外数据有限，室内和室外测试之间的相关性也不明确，因此长期稳定性是包晶光伏技术商业化的主要挑战。在本研究中，我们报告了两年来对 perovskite 微型模块进行的室外稳定性测试。我们对全天的性能变化进行了详细分析，量化了昼夜衰减和夜间恢复。此外，我们还采用了 XGBoost 回归模型来预测功率输出。我们对大量老化数据进行的统计分析显示，所有测试的包晶配置都表现出昼夜衰减和恢复，在所有环境条件下，这些阶段之间都保持着线性关系。我们的预测模型以基本环境参数为重点，以 6.76% 的 nRMSE 准确预测了微型模块的功率输出，这表明该模型具有预测基于包晶石的器件寿命的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACS Energy Letters Energy-Renewable Energy, Sustainability and the Environment

CiteScore

31.20

自引率

5.00%

发文量

469

审稿时长

1 months

期刊介绍： ACS Energy Letters is a monthly journal that publishes papers reporting new scientific advances in energy research. The journal focuses on topics that are of interest to scientists working in the fundamental and applied sciences. Rapid publication is a central criterion for acceptance, and the journal is known for its quick publication times, with an average of 4-6 weeks from submission to web publication in As Soon As Publishable format. ACS Energy Letters is ranked as the number one journal in the Web of Science Electrochemistry category. It also ranks within the top 10 journals for Physical Chemistry, Energy & Fuels, and Nanoscience & Nanotechnology. The journal offers several types of articles, including Letters, Energy Express, Perspectives, Reviews, Editorials, Viewpoints and Energy Focus. Additionally, authors have the option to submit videos that summarize or support the information presented in a Perspective or Review article, which can be highlighted on the journal's website. ACS Energy Letters is abstracted and indexed in Chemical Abstracts Service/SciFinder, EBSCO-summon, PubMed, Web of Science, Scopus and Portico.