SolarClique: Detecting Anomalies in Residential Solar Arrays
SolarClique: Detecting Anomalies in Residential Solar Arrays
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SolarClique：检测住宅太阳能电池阵列的异常情况
DOI：
10.1145/3209811.3209860
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Prashant J. Shenoy
中科院分区：
文献类型：
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作者：
Srinivasan Iyengar;Stephen Lee;D. Sheldon;Prashant J. Shenoy
多年来，太阳能部署的扩散显著增加。分析这些部署可以及时检测发电异常，从而最大限度地发挥太阳能的优势。在本文中，我们提出了SolarClique，这是一种数据驱动的方法，可以高精度地标记发电异常。与以前的方法不同，我们的工作既不依赖于昂贵的仪器，也不需要外部输入，如天气数据。相反，我们的方法利用地理上邻近的站点的太阳能发电的相关性来预测站点的预期输出并标记异常。我们评估我们的方法对88太阳能装置位于得克萨斯州奥斯汀。我们表明，我们的算法甚至可以使用来自几个地理位置附近的站点（>5个站点）的数据来产生高精度的结果。因此，我们的方法可以扩展到人口稀少的地区，那里几乎没有太阳能装置。此外，在88个装置中，我们的方法报告了76个发电异常的地点。此外，我们的方法足够强大，可以区分由于异常和其他因素（如多云条件）导致的功率输出减少。
The proliferation of solar deployments has significantly increased over the years. Analyzing these deployments can lead to the timely detection of anomalies in power generation, which can maximize the benefits from solar energy. In this paper, we propose SolarClique, a data-driven approach that can flag anomalies in power generation with high accuracy. Unlike prior approaches, our work neither depends on expensive instrumentation nor does it require external inputs such as weather data. Rather our approach exploits correlations in solar power generation from geographically nearby sites to predict the expected output of a site and flag anomalies. We evaluate our approach on 88 solar installations located in Austin, Texas. We show that our algorithm can even work with data from few geographically nearby sites (>5 sites) to produce results with high accuracy. Thus, our approach can scale to sparsely populated regions, where there are few solar installations. Further, among the 88 installations, our approach reported 76 sites with anomalies in power generation. Moreover, our approach is robust enough to distinguish between reduction in power output due to anomalies and other factors such as cloudy conditions.