Indoor Perovskite Solar Cells (IPSCs) have recently gathered massive research attention, driven by their promising role in powering the continuously expanding Internet of Things (IoT) devices and simultaneous advancements in the Perovskite solar field. To further accelerate the development of IPSCs, a machine learning (ML) approach to assist the advancement of IPSCs is proposed in the current study. Here, a ML model to predict the most important performance parameters such as short circuit current (JSC), open circuit voltage (VOC), fill factor (FF), and power conversion efficiency (PCE) of IPSCs under various light sources and intensities is presented. This developed model can effectively predict the performances of Perovskite Solar Cells (PSCs) operated under indoor illumination close to the true/experimental values. The factors affecting the IPSC performance by Correlation matrix and SHAPley analysis are also analyzed. These findings demonstrate that the proposed ML model provides accurate predictions of VOC, JSC, FF, and PCE of IPSCs, ultimately contributing to the optimization of solar cell performance under indoor environments and the advancement of renewable energy technology.

中文翻译：

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机器学习指导策略开发高效室内钙钛矿太阳能电池

室内钙钛矿太阳能电池（IPSC）最近引起了广泛的研究关注，因为它们在为不断扩展的物联网（IoT）设备供电方面发挥着广阔的前景，同时钙钛矿太阳能领域也取得了进步。为了进一步加速 IPSC 的发展，本研究提出了一种机器学习（ML）方法来协助 IPSC 的发展。这里，ML模型用于预测最重要的性能参数，例如短路电流（JSC）， 开路电压 （V奥克）、填充因子（FF）和不同光源和强度下IPSC的功率转换效率（PCE）。该模型可以有效预测钙钛矿太阳能电池（PSC）在室内照明下的性能，接近真实/实验值。还通过相关矩阵和SHAPley分析分析了影响IPSC性能的因素。这些发现表明，所提出的 ML 模型可以准确预测V奥克,JSCIPSC的、FF和PCE，最终有助于室内环境下太阳能电池性能的优化和可再生能源技术的进步。