The analysis of electrical load signatures is an enabling technology for many applications, such as ambient assisted living or energy-saving recommendations. Through the digitalization of electricity metering infrastructure, meter reading intervals are gradually becoming more frequent than the traditional once-per-year reporting. In fact, across smart meter generations, samples were initially reported in 15-min intervals, more recently once per second, and even newer devices capture readings at rates on the order of several kilohertz. The advantages of using such high sampling rates have, however, not been unambiguously demonstrated in literature. We thus choose a widely considered application scenario of energy data analytics, event detection, and assess the impact of the sampling rate choice on the correct event recognition rate. More specifically, we compare the accuracy of two event detection algorithms with respect to the resolution of their input data. The results of our analysis hint at a non-linear relation between accuracy and data resolution, yet also indicate that most event occurrences can be correctly determined when using a sampling rate of approximately 1 kHz, with only minimal improvements achievable through higher rates.

中文翻译：

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数据采样率对负载特征事件检测的影响研究

电气负载信号分析是许多应用程序的使能技术，例如环境辅助生活或节能建议。通过电表基础设施的数字化，抄表间隔正逐渐变得比传统的每年一次的报告更为频繁。实际上，在几代智能电表中，最初以15分钟为间隔报告一次样本，最近一次是每秒报告一次，甚至更新的设备也以几千赫兹的速率捕获读数。但是，在文献中并未明确证明使用这种高采样率的优势。因此，我们选择了能源数据分析，事件检测的广泛考虑的应用场景，并评估了采样率选择对正确事件识别率的影响。更具体地说，我们比较两种事件检测算法相对于其输入数据分辨率的准确性。我们的分析结果暗示了准确性和数据分辨率之间的非线性关系，但同时也表明，当使用大约1 kHz的采样率时，大多数事件发生都可以正确确定，而通过较高的采样率只能实现最小的改进。