Abstract
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.
Highlights
In recent years, a global trend towards the roll-out of smart metering infrastructure can be observed
The F1 score is highest for the input data that has not undergone a resolution reduction
Our evaluations have shown that only small differences of the F1 score can be observed when comparing event detection results on data at their native resolution (12kHz) to input data that have been reduced by a factor of approximately 10
Summary
A global trend towards the roll-out of smart metering infrastructure can be observed. A widely researched application domain for the data collected through smart meters is Non-Intrusive Appliance Load Monitoring (NIALM) (Hart 1992). It follows the objective of determining the operative appliances and their modes of operation from a household’s aggregate power consumption. An important component in NIALM algorithms is the recognition of state or mode of operation changes, referred to as event detection in literature (Chang et al 2011; Bijker et al 2009). The temporal resolution at which data is provided to an event detection algorithm can differ widely. Using the lowest possible data resolution is, an important objective to minimize the demand for storage space and computation time.
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