Hi, I'm Nadine von Frankenberg. My dissertation topic is in the field
of thermal comfort. Thermal comfort is the satisfaction with the thermal environment,
which is important because it affects our health and productivity.
Regulating temperatures in homes is easy because you can set up everything to your
own preferences. In shared spaces, like offices, the story is a bit different.
Let say, we have 7 occupants sharing one thermostat.
They all have different preferences regarding the temperature. This is what we define as a conflict,
a disagreement about the temperature between one or multiple occupants
and the environment. This conflict cannot be solved easily so that everyone is comfortable.
Many building management systems use models to regulate the temperature.
Many of these models are based on data-driven approaches, such as the Predicted Mean Vote (PMV)
and machine learning models, however, they require large datasets
and continuous occupant feedback, which is hard to obtain in practice.
Occupants often also need to understand decisions why the temperature has or hasn't changed.
For instance, imagine you just voted for a warmer temperature, but instead the temperature was lowered.
In general, these systems tend to bypass conflict resolution through rule-based behavior.
This leads to my main research question, how can we resolve thermal comfort
conflicts in shared spaces in a fair manner? First, conflicts need to be identified based on event processing.
Next, we need to identify factors that might contribute to the conflict, such as somebody sitting next to a window where the sun shines through, an open window, or a fan, and
and take those into consideration when making and justifying a decision. As these decisions are made to increase
the thermal comfort of occupants, they need to be kept in the loop.
And Rationale Management allows us to include all of these factors.
These abstractions are separated into three levels. First, during the event processing,
the events are categorized to filter out events that lead to potential conflicts,
for instance if an occupant votes for a warmer temperature or a cooler temperature. If a conflict is found,
the context indicators, such as who is involved, what is their schedule, what are the environmental
conditions, are identified. In the third level, the conflict is described using a rationale model
that includes the proposed decision, alternative decisions and arguments. This decision is
evaluated based on suitable solution strategies, like bidding or different prioritizations, and other policies.
At the end, the occupant can review the decision and give feedback as a retrospective.
We implemented this as the TREATI framework, which has an open IoT architecture. TREATI uses
a multi-abstraction process that focuses on rationale
management efforts to resolve thermal comfort conflicts in shared spaces.
I follow a mixed-methods research approach that
focuses on the validation and verification of TREATI.
We implemented each level in different projects. The goal was to show the validity
of each abstraction level and to find shortcomings in the process. We further evaluated each level separately.
In addition, we conducted a survey that showed the necessity for different levels of abstraction
and occupant involvement in the decision-making process.
We asked questions to understand the occupants' self-perceived behavior in conflicts and the control behavior they would expect.
Testing and evaluating TREATI would require many resources and take a long time, while putting
occupants at risk when testing extreme situations, like constantly lowering or increasing the temperature. Thus, we use a simulation study to verify TREATI
against its simulation model and validate it against the real world. So far, we've conducted a simplified
simulation which has shown a few inconsistencies that we rectified in a second iteration.
Next steps are to conduct a more detailed simulation, finish my dissertation, and of course also conduct a human subject experiment, as soon as the COVID-19 situation allows it.
To learn more about my evaluation and results, please join my poster session.
Thank you.