Event-driven metering is an emergent paradigm enabling significant data compression enhancement with respect to the conventional time domain metering techniques. This paper first discusses the representation of energy-based data in low-voltage segments of smart grids by using a process-oriented approach, providing an original interpretation in terms of accumulated energy. This interpretation is used to construct an overall framework containing multiple options for representing energy-based data, encompassing uniform and nonuniform linear time finite elements. A specific representation of the information defined in the space of digital events is then presented under the established framework.
An enhanced representation of energy-based data identified as digital events is introduced, leading to the formulation of the final event-based data gathering (EBDG) scheme. Dedicated tests are carried out on exemplificative data sets for residential customers, including a new challenging benchmark developed by using a fuzzy-controlled load for air conditioning. The results obtained validate the use of the EBDG scheme to mine the energy-varying processes occurring at the remote nodes of the distribution system. Finally, the approach leading to the EBDG scheme is discussed to show how it falls within the realm of Big Data and to highlight how the EBDG results can be used to enable analytic accounting of the energy consumed in the processes occurring in a given time interval.