Establishment of Enhanced Load Modeling by Correlating With Occupancy Information

Over the past decades, ther have been an increased number of the Internet of Things (IoT) sensor deployment in electrical distribution networks. This paper proposes a statistical approach to establish the correlations between estimated occupancy within physical proximity and the associated loads. This study includes a sensitivity analysis of occupancy and how it influences load consumptions. First, a statistical distribution with a regression model is formed to correlate these two heterogeneous properties, in terms of occupancy and power (OP) consumption, to generate a time-dependent model. Since model deviations from an estimate based on regular pattern as a time-varying process with the curve fitting, the possible structural deviations in real-time demand are then considered to update the initial regression model using new real-time estimates and observations obtained every demanded time duration. The dynamic profile of human movements and their load characteristics are established with parametric adjustments and observed using the case studies.