Electrified heating and mobility, the uptake of air conditioning and distributed energy resources are reshaping residential electricity demand and will require substantial investment. Yet the dependencies that drive present and future residential demand across sociodemographic characteristics, occupant activities, energy service demands, local technologies, and interactions with the overarching energy system remain poorly understood. Activity-based, bottom-up models make these dependencies explicit, better informing flexible operation and investment in low-carbon technologies. We review 45 activity-based residential models and assess coverage of appliances, domestic hot water, space heating and cooling, and mobility (electric vehicle charging), which are rarely considered jointly in one integrated model. To our knowledge, this is the first review to include activity-based mobility modeling, thereby identifying methodological gaps in consistent behavior modeling across residential energy services: First, most studies simulate single occupants in isolation rather than entire households, thereby overlooking interdependencies among occupants. Second, predominant use of Markov models or independent univariate sampling limits temporal consistency. Based on these findings, future studies should combine complementary behavioral datasets with sophisticated models (e.g., deep neural networks) capable of capturing complex dependencies to generate high-quality synthetic behavioral data as a basis for future bottom-up residential energy demand modeling. Further progress requires open datasets and reproducible validation frameworks to benchmark and compare activity-based models and to ensure consistent progress in the field. Currently, there is no model available in the literature that derives energy demand for thermal comfort, hot water, mobility, and other services consistently from one fundamental representation of household behavior.
Vogl et al. (Wed,) studied this question.