The number of globally deployed smart meters is rising, and so are the sampling rates at which they can meter electrical consumption data. As a consequence thereof, the technological foundation is established to track the power intake of buildings at sampling rates up to several
. Processing raw signal waveforms at such rates, however, imposes a high resource demand on the metering devices and data processing algorithms alike. In fact, the ensuing resource demand often exceeds the capabilities of the embedded systems present in current-generation smart meters. Consequently, the majority of today’s energy data processing algorithms are confined to the use of RMS values of the data instead, reported once per second or even less frequently. This entirely eliminates the spectral characteristics of the signal waveform (i.e., waveform trajectories of electrical voltage, current, or power) from the data, despite the wealth of information they have been shown to contain about the operational states of the operative appliances. In order to overcome this limitation, we pursue a novel approach to handle the ensuing volume of load signature data and simultaneously facilitate their analysis. Our proposed method is based on approximating the current intake of electrical appliances by means of parametric models, the determination of whose parameters only requires little computational power. Through the identification of model parameters from raw measurements, smart meters not only need to transmit less data, but the identification of individual loads in aggregate load signature data is facilitated at the same time. We conduct an analysis of the fundamental waveform shapes prevalent in the electrical power consumption data of more than 50 electrical appliances, and assess the induced approximation errors when replacing raw current consumption data by parametric models. Our results show that the current consumption of many household appliances can be accurately modeled by a small number of parameterizable waveforms.
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