Relating DR to Occupancy

How can we begin to understand individual occupant behavior in a building? In what context is modeling individual behavior needed or is aggregated behavior more practical? Literature suggests in households, modeling an individual’s activity stream can capture the logical structure of energy behavior. Averaged schedules and consumption behaviors are insufficient and may not incorporate covariant end uses. In the case of low-energy or passive buildings especially, occupants completely drive the internal gains and hence energy demands. Simulations of daily behavior (extendable to multiple occupants) can then replicate daily household energy consumption.

For defining a DR potential model, Hae Young and I talked about how to distinguish “flexible” from “inflexible” behaviors, and how the data we can currently obtain might reflect that. Because “flexibility” is more of a spectrum and can be altered by the price, the relationships are not necessarily a hierarchy. Rather, I think putting it in this chart can help organize how we can think about the ends of the spectrum, understand sensor data, and interpret some meaning.

To explain the chart, the energy consumption data that would be collected from every measured load could show a range of activity, from showing strong, fixed patterns to appearing completely random in time and duration. Thus, splitting these into two categories of extremes, we can look at its relationship to occupancy information. Based on how correlated the energy consumption is to the occupancy level or status could mean many things about the flexibility of that load. Correlation could entail active occupancy is linked to the activity or the response could be delayed, where an activity or action does not consume energy until some time later or persists after the occupant(s) have left, such as running a laundry machine. Therefore, different ways of correlating activity is necessary.

In the first category, fixed consumption patterns could be a result of either a strong preference to use that device or appliance at some fixed time or because the load is really a baseload that is essentially inflexible. Of course, if such a device or appliance were inefficient, modifying its operation would be possible and be a low-cost source of DR potential. In a perfect system, these inefficiencies may be assumed to have already been removed. Although in reality, there are many barriers that may make temporarily reducing inefficiencies more viable than permanent reductions. Therefore, controlling loads with fixed consumption patterns would appear to translate into a function of cost. In the correlated case, that cost would be related to the user’s cost of comfort whereas in the other case, the cost is tied to long-term, user-aggregated, or overall building costs. An example would be the cycling of the refrigerator which has potential long-term maintenance costs and distributed impacts to all who share its usage.

In the second category, seemingly random consumption of an appliance or device can similarly be divided into its relationship to occupancy. Correlations with occupant behavior indicate it is dependent on user demand (e.g. lighting) and hence its flexibility will be price-driven. The discomfort cost is higher for some than others and will depend on the value of the activity itself. However, in the uncorrelated case, the usage of the device of appliance may not at all matter to the occupant and hence is likely to have a low impact on comfort and should be highly flexible. To be careful, though, the costs may not be linear in the sense that although doing laundry in 1 hour vs 5 hours makes little difference, not having it done by the next day at a certain time may in fact incur high discomfort costs. Capturing and predicting such preferences will be a challenge given that current appliances generally do not have such an option to express this preference. The main challenge in assessing DR potential in the uncorrelated case is predicting when such random activities occur so that they can be shifted.

As a next task, can experimental data fit such a representation?  Would such a representation be rational and useful down the road?