This paper presents a data mining procedure to generate a typical daily shape of energy consumption based on smart metering measurements. Such a typical daily shape describes an observed or estimated graphical representation of the variation of the electric load against time. This paper proposes two methods for the development of the novel data mining procedure: i) statistical analysis applied to a set of measured data; ii) cluster analysis using the machinelearning algorithm to determine a typical daily shape. The new procedure can be part of the Smart Metering and more precisely of the Automated Meter Reading (AMR). The results of the statistical analysis using the set of measured data show whether or not the average values are representative for the data set. The results of the cluster analysis extend the potential of forecasting a typical daily shape and of offering tools for potential applications to data mining procedure.
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