Abstract
Metering of the energy supplied to consumers is an important component of operations for utility providers. Several schemes have been employed for this purpose, including traditional postpaid and prepaid metering, and more advanced smart metering technology. Analysis of the data generated by these meters has the potential to provide insights into consumer characteristics and power consumption patterns, including consumer segmentation and anomaly detection. We describe the different types of power purchase and consumption data, as well as the analytics algorithms that can be applied to them. Most applications developed for energy meter data require high resolution information of the type provided by smart meters, thus leaving aggregate prepaid or postpaid meter schemes at a disadvantage. In this paper, we present analytics-based methodologies to upgrade aggregate prepaid and postpaid meter data resolution, which will allow smart meter analytics to be applied without expensive infrastructure upgrades.
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