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This paper proposes a novel feature construction methodology aiming at both clustering yearly load profiles of low-voltage consumers, as well as investigating the stochastic nature of their peak demands. These load profiles describe the electricity consumption over a one-year period, allowing the study of seasonal dependence. The clustering of load curves has been extensively studied in literature, where clustering of daily or weekly load curves based on temporal features has received the most research attention. The proposed feature construction aims at generating a new set of variables that can be used in machine learning applications, stepping away from traditional, high dimensional, chronological feature sets. This paper presents a novel feature set based on two types of features: respectively the consumption time window on a daily and weekly basis, and the time of occurrence of peak demands. An analytic expression for the load duration curve is validated and leveraged in order to define the the region that has to be considered as peak demand region. The clustering results using the proposed set of features on a dataset of measured Flemish consumers at 15-min resolution are evaluated and interpreted, where special attention is given to the stochastic nature of the peak demands.

A Novel Feature Set for Low-Voltage Consumers, Based on the Temporal Dependence of Consumption and Peak Demands