  Load points are one of the most vital parts of power systems. Due to the new
load forms and programs introduced in the demand side, the load-serving
entities (LSEs) no longer deal with lump loads, but rather with more dynamic,
rational and price elastic loads. The high inter-temporal and behavioral
variability of the load profile makes it almost impossible for utilities and
system operators to expect the demand curve with the needed accuracy. A sound
granularity of the load compositions and consumption percentages and patterns
throughout the year is essential for avoiding energy losses, designing
demand-side management programs, and ensuring proper adjustments of electricity
rates. In this paper, a simplistic model that can be followed by system
operators to initially understand the customers' consumption pattern and the
household load structure is proposed. A top-down approach is combined and
matched with a detailed bottom-up one, to extract load compositions and
percentages. Real and local load profiles integrated with household statistical
data such as device time of use (ToU), number of device units per house, and
activities exercised in households are all included in the model. The main
results of the paper show the load composition in residential demand and the
percentage of such composition under seasonal-based scenarios.
