Accurate prediction of the future performance and remaining useful lifetime of next-generation solar cells such as organic photovoltaics (OPVs) is necessary to drive better designs of materials and ensure reliable system operation. Degradation is multifactorial and difficult to model deterministically; however, with the advent of machine learning, data from outdoor performance monitoring can be used for understanding the relative impact of stress factors and could provide a powerful method to interpret large quantities of outdoor data automatically. Here, we propose the use of artificial neural networks and regression models for forecasting OPV module performance and their degradation as a function of climatic conditions. We demonstrate their predictive capability for short-term energy forecasting of OPV modules, showing that energy yield can be predicted if climatic conditions are known. In addition, the model has been extended so that the impact of climatic conditions on degradation can be predicted. The combined model has been validated on unseen OPV module data and is able to predict energy yield to within 4% accuracy.

中文翻译：

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通过机器学习预测有机光伏的昼夜户外性能和退化；与室外压力条件相关的退化

准确预测下一代太阳能电池（如有机光伏（OPV））的未来性能和剩余使用寿命对于推动更好的材料设计和确保可靠的系统运行是必要的。退化是多因素的，难以确定性地建模；然而，随着机器学习的出现，来自户外性能监测的数据可用于了解压力因素的相对影响，并可以提供一种强大的方法来自动解释大量户外数据。在这里，我们建议使用人工神经网络和回归模型来预测 OPV 模块性能及其作为气候条件函数的退化。我们展示了他们对 OPV 模块短期能量预测的预测能力，表明如果气候条件已知，则可以预测能量产量。此外，该模型已得到扩展，以便可以预测气候条件对退化的影响。组合模型已在未见的 OPV 模块数据上得到验证，并且能够以 4% 的精度预测能量产量。