Building occupants are a crucial part of any building energy demand model. On the one hand, their activities in create the demands for lighting, appliances and domestic hot water in buildings. Their presence also drives the control of the building systems, as ventilation is only provided when occupants are present and the room temperature is also adjusted when occupants are present or not (i.e., whether the building set point or set back temperature is maintained). Occupants also create building loads through the heat they irradiate into the room as well as the humidity they produce.

Due to their important role in the simulation, students and first time users often ask me about the occupant schedules in CEA, how they are defined and what the differences are between the two occupant models included in the tool: deterministic vs. stochastic. In this series of posts, we will explore the two occupant models included in CEA, what the main differences are between them and what their limitations are, as well as providing a peek into what is coming up in the near future. For more technical information on CEA models, you might like to have a look at the corresponding tutorial.

**Deterministic occupant modeling in CEA**

The simplest form of occupant modeling is the standard deterministic method. As described in a previous blog post, the CEA archetype database includes a number of building schedules for different building occupancy types taken from relevant standards. As an example, here are the CEA schedules for school buildings in Switzerland:

The schedules for offices comprises the following: hourly probabilities of occupant presence on weekdays, Saturdays and Sundays; hourly probabilities of lighting and appliance use on weekdays, Saturdays and Sundays; hourly probabilities of domestic hot water use on weekdays, Saturdays and Sundays; monthly probabilities of occupancy throughout the year; and the occupant density for a given building function.

For each occupancy type in a building, the maximum number of occupants is defined based on the occupancy density, and at each time of day the number of occupants is calculated from the above schedule. So, for example, if we have an office building with a floor area of 1400 m², at 9 am on a weekday in August there would be 24 people present (1400 m² / [14 m²/p] * 0.4 * 0.6). The number of occupants for mixed-use building are therefore simply the sum of the number of occupants for each of the occupancy types in the building.

The electricity demand for lighting and appliances is calculated in a similar manner, but instead of the occupant density, the power density for lighting and appliances (in W/m²) is used. For example, power density for appliances in an office according to the CEA database is 7 W/m². So at 9 am on a weekday in August the aforementioned office would have a demand of 2352 Wh (1400 m² * 7 W/m² * 0.4 * 0.6). The deterministic model in CEA calculates a demand pattern for the entire building as an average of all the occupancy types in the building, however users may adjust the peak power demand for lighting and appliances in the inputs to their project.

Finally, the demand for domestic hot water and fresh water in CEA are defined in the inputs in terms of liters of water per person per day (L/p/d). Since the CEA database provides hourly probabilities of domestic hot water use, the actual amount of water used is calculated as a normalized hourly probability (i.e., the probability of domestic hot water use at a given hour divided by the sum of the probabilities of domestic hot water use at each hour of the day). According to the CEA archetypes, the demand for domestic hot water in offices is 10 liters per person per day. So the amount of domestic hot water use in the aforementioned example at 9 am on a weekday in August would be 16 liters (10 L/p * 24 p * 0.4 / [0.2 + 0.4 + 0.6 + 0.8 + 0.8 + 0.4 + 0.6 + 0.8 + 0.8 + 0.4 + 0.2]). The demand for domestic hot water and fresh water for mixed-use buildings would thus be calculated as the sum of the demands for each of the occupancy types in the building given the number of occupants present in each. Once again, the user may adjust the liters per person for a given building in the building inputs while maintaining a mixed-use water use pattern.

In the next blog post, we will have a look at the stochastic model available in CEA as an option to the deterministic model.