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首页> 外文会议>2016 IEEE Nordic Circuits and Systems Conference >Solar panel modelling for low illuminance indoor conditions
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Solar panel modelling for low illuminance indoor conditions

机译：用于低照度室内条件的太阳能电池板建模

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摘要

In the energy harvesting domain, the modelling of a solar panel plays an important role in predicting the energy availability of energy harvesting system applications. Indoor environments, which are illuminated by artificial light sources, have typically much lower illumination levels than outdoor environments. In this paper, we compare the behaviour of different types of models under low illuminance conditions, in order to investigate sufficient modelling approaches for indoor environments. Previous work has shown that equivalent circuit modelling may have reduced performance under low illuminance conditions. Instead, we investigate behavioural models and compare their results with the equivalent circuit model. Two different types of behavioural models have been tested, namely artificial neural network models and polynomial curve fitting models. The comparison of these three models has shown that it is not possible to establish which of the modelling methods performs best, because each of them have strong points and shortcomings making the ideal choice application dependant.
机译：在能量收集领域，太阳能电池板的建模在预测能量收集系统应用的能量可用性方面起着重要作用。由人造光源照明的室内环境通常比室外环境要低得多的照明水平。在本文中，我们比较了低照度条件下不同类型模型的行为，以研究室内环境的足够建模方法。先前的工作表明，等效电路建模可能会降低低照度条件下的性能。相反，我们研究行为模型，并将其结果与等效电路模型进行比较。测试了两种不同类型的行为模型，即人工神经网络模型和多项式曲线拟合模型。对这三个模型的比较表明，无法确定哪种建模方法效果最佳，因为每种方法都有其长处和短处，因而使理想的选择应用程序产生依赖性。

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