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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.