Residential buildings account for 20% of the nation’s total energy consumption. Although residential buildings represent a high energy demand, their high energy saving potential has not been reached. One of the most significant obstacles to reaching these potentials is the lack of understanding of building-occupant interactions. A deep understanding of occupant behavior is essential in lowering the energy consumption of residential homes. Since predicting occupant behavior seems difficult to discern, this study will use a different approach to identify the occupants behavior on building system usage with various sensors. For this purpose, we installed sensors to detect the residential building system usage, such as Heating, Ventilation and Air Conditioning (HVAC), window usage, cooktop usage, etc. HVAC systems consume a large portion of the residential energy consumption. However, it is not easy to detect the HVAC system usage. Therefore, we developed an algorithm to define the setpoint temperature and HVAC system run time based on the measured data (room temperature, outdoor air temperature, and supply grille air temperature). We will use the supply grille air temperature change over time (dT/dt) as an indicator of the HVAC system being on or off. The relationship between set point temperature and HVAC run time is the main driving force of how much energy is being consumed by the residential building. This paper employs the use of Python as well as various data analysis tools to analyze energy consumption in various types of homes.