Sampling density and frequency as data quality determinants in smart grids

The integration of smart devices and sensors together with extensive data processing capabilities into the electrical power grid is a fundamental infrastructure of Smart Grid's environments. Such capabilities introduce major Data Quality challenges: The optimization of electricity production and consumption in such environments relies on collecting and analyzing vast amounts of sensor-based data samples in real time. Degradation in the quality of such data might hinder its analysis and result in sub-optimal Smart Grid configuration. This study aims at exploring the effect of two Data Quality determinants in Smart Grid's environments — sampling frequency, reflecting the temporal distribution, and sampling density, reflecting spatial distribution. Beyond technical aspects, sampling density and frequency have economic implications, which must affect their optimal configuration. This study contributes to further conceptualization of these Data Quality determinants and assessing their impact in Smart Grid's environments, by developing an analytical model that links their configuration to cost-benefit tradeoffs. This manuscript presents the model development, and its preliminary evaluation with a large-scale datasets that reflects energy consumption in a real-world environment.