Solar thermal systems (STS) present a promising avenue for low-carbon heat generation, with a well-running system providing heat at minimal cost and carbon emissions. However, STS can exhibit faults due to improper installation, maintenance, or operation, often resulting in a substantial reduction in efficiency or even damage to the system. As monitoring at the individual level is economically prohibitive for small-scale systems, automated monitoring and fault detection should be used to address such issues. Recent advances in data-driven anomaly detection, particularly in time series analysis, offer a cost-effective solution by leveraging existing sensors to identify abnormal system states. Here, we propose a probabilistic reconstruction-based framework for anomaly detection. We evaluate our method on the publicly available PaSTS dataset of operational domestic STS, which features real-world complexities and diverse fault types. Our experiments show that reconstruction-based methods can detect faults in domestic STS both qualitatively and quantitatively, while generalizing to previously unseen systems. We also demonstrate that our model outperforms both simple and more complex deep learning baselines. Additionally, we show that heteroscedastic uncertainty estimation is essential to fault detection performance. Finally, we discuss the engineering overhead required to unlock these improvements and make a case for simple deep learning models.

翻译：太阳能热系统（STS）为低碳热能生产提供了前景广阔的途径，运行良好的系统能以最低成本和碳排放提供热能。然而，由于安装、维护或操作不当，STS可能出现故障，通常导致效率显著降低甚至系统损坏。由于对小型系统进行个体监测在经济上不可行，应采用自动化监测与故障检测技术来解决此类问题。数据驱动异常检测的最新进展，特别是在时间序列分析领域，通过利用现有传感器识别异常系统状态，提供了一种经济高效的解决方案。本文提出了一种基于概率重构的异常检测框架。我们在公开可用的家用STS运行数据集PaSTS上评估了该方法，该数据集具有真实世界的复杂性和多样化的故障类型。实验表明，基于重构的方法能够定性和定量地检测家用STS中的故障，并能泛化到未见过的系统。我们还证明，该模型优于简单和复杂的深度学习基线方法。此外，我们证明了异方差不确定性估计对故障检测性能至关重要。最后，我们讨论了实现这些改进所需的工程开销，并论证了简单深度学习模型的应用价值。