Occupants’ presence and activity schedules directly influence residential energy consumption loads. Regardless of their widely acknowledged importance, developing proper representative occupancy inputs for urban energy use studies of residential neighborhoods remains to be a challenge to overcome. The presented work aims to balance between accuracy and complexity of such occupancy models by developing a technique that takes advantage of a previously proposed sophisticated method for schedule generation and attempts to refine and simplify its results for practicality purposes.

Here, we used a Markov chain transition probability matrix based on the American Time-Use Survey (ATUS) database and selectively refined its outputs according to the data collected from our own designated population of study. The resulting refined schedules were incorporated into the Urban Modeling Interface (umi) interface and were then tested on our pilot case study, a relatively low-income dense neighborhood in the Midwestern United States composed of 272 residential buildings. An initial investigation of this technique’s performance suggests that while the use of the ATUS based model provided a high level of variability and sophistication, the customization step ensured that the resulting schedules are representative of our population and its characteristics. More importantly, we were able to maintain simplicity and practicality.