<p>Commercial buildings consume more than 19% of the total
energy consumption in the United States. Most of this energy is consumed by the
HVAC and shading/lighting systems inside these buildings. The main purpose of
such systems is to provide satisfactory thermal and visual environments for
occupants working inside these buildings. Providing satisfactory thermal/visual
conditions in indoor environments is critical since it directly affects
occupants’ comfort, health and productivity and has a significant effect on
energy performance of the buildings. </p>
<p>Therefore, efficiently learning occupants’ preferences is of
prime importance to address the dual energy challenge of reducing energy usage
and providing occupants with comfortable spaces at the same time. The objective
of this thesis is to develop robust and easy to implement algorithms for
learning and eliciting thermal and visual preferences of office occupants from
limited data. As such, the questions studied in this thesis are: 1) How can we
exploit concepts from utility theory to model (in a Bayesian manner) the hidden
thermal and visual utility functions of different occupants? Our central
hypothesis is that an occupant’s preference relation over different
thermal/visual states of the room can be described using a scalar function of
these states, which we call the “occupant’s thermal/visual utility function.”
2) By making use of formalisms in Bayesian decision theory, how can we learn
the maximally preferred thermal/visual states for different occupants without
requiring unnecessary or excessive efforts from occupants and/or the building
engineers? The challenge here is to minimize the number of queries posed to the
occupants to learn the maximally preferred thermal/visual states for each
occupant. 3) Inferring preferences of occupants based on their responses to the
thermal/visual comfort-based questionnaire surveys is intrusive and expensive.
Contrary to this, how can we learn the thermal/visual preferences of occupants
from cheap and non-intrusive human-building interactions’ data? 4) Lastly,
based on the observation that the occupant population decompose into different
clusters of occupants having similar preferences, how can we exploit the collective
information obtained from the similarities in the occupants’ behavior? This
thesis presents viable answers to the aforementioned questions in the form of
probabilistic graphical models/frameworks. In future, I hope that these
frameworks would prove to be an important step towards the development of
intelligent thermal/visual systems which would be able to respond to occupants’
personalized comfort needs. Furthermore, in order to encourage the use of these
frameworks and ensure reproducibility in results,various implementations of
this work (namely GPPref, GPElicit and GPActToPref) are published as
open-source Python packages.</p><br>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/19127135 |
| Date | 07 February 2022 |
| Creators | Nimish M Awalgaonkar (12049379) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/BAYESIAN_METHODS_FOR_LEARNING_AND_ELICITING_PREFERENCES_OF_OCCUPANTS_IN_SMART_BUILDINGS/19127135 |
Page generated in 0.0068 seconds