To fully understand the impact residential end-use technologies can have on the integrated energy network, advanced modeling of the interactions between customers and end-use technologies is required. The current state of the art is for utilities to develop 8760 load shapes that can be used for system planning, designing rate structures, and developing programs for energy efficiency and efficient electrification measures. To accomplish this, utilities either rely on metered data (which can be costly to collect at the end-use level) or modeled data. In this study, a bottom-up modeling approach is presented in which the characteristics of the home, its individual end-uses, and the behavior of its occupants are modeled. Using this detailed methodology, the contribution of each end-use toward the overall aggregate demand of the residential sector can be identified. As a basis for this modeling work, occupant behavior models are developed using data from the American Time Use Survey to create a statistically accurate representation of how customers interact with major residential end-uses. These models are simulated using the Markov chain Monte Carlo method and predict behavior based on the time of day and day of the week on a minute-by-minute basis. To estimate energy consumption, detailed characterizations of major residential end-uses and their interactions with customers and external environmental conditions have been developed. Finally, to demonstrate some of the potential applications of this framework, a variety of modeling use-cases are presented.