<p>In perimeter building zones with glass
façades, controllable fenestration (daylighting/shading) and electric lighting
systems are used as comfort delivery systems under dynamic weather conditions,
and their operation affects daylight provision, outside view, lighting energy
use, as well as overall occupant satisfaction with the visual environment. A
well-designed daylighting and lighting control should be able to achieve high
level of satisfaction while minimizing lighting energy consumption. Existing daylighting
control studies focus on minimizing
energy use with general visual comfort constraints, when adaptive and
personalized controls are needed in high performance office buildings.
Therefore, reliable and
efficient models and methods for learning occupants’ personalized visual preference
or satisfaction are required, and the development of optimal daylighting
controls requires integrated considerations of visual preference/satisfaction
and energy use. </p>
<p>In this Dissertation, a novel
method is presented first for developing personalized visual satisfaction
profiles in daylit offices using Bayesian inference. Unlike previous studies
based on action data, a set of experiments with human subjects was designed and
conducted to collect comparative visual preference data (by changing visual conditions)
in private offices. A probit model structure was adopted to connect the
comparative preference with a latent satisfaction utility model, assumed in the
form of a parametrized Gaussian bell function. The distinct visual satisfaction
models were then inferred using Bayesian approach with preference data. The
posterior estimations of model parameters, and inferred satisfaction utility
functions were investigated and compared, with results reflecting the different
overall visual preference characteristics discovered for each person.</p>
<p>Second, we present an online visual preference elicitation
learning framework for efficiently learning and eliciting occupants’
visual preference profiles and hidden satisfaction utilities. Another set of experiments with human
subjects was conducted to implement the proposed learning algorithm in order to
validate the feasibility of the method. A combination of Thompson sampling and
pure exploration (uncertainty learning) methods was used to balance exploration
and exploitation when targeting the near-maximum area of utility during the
learning process. Distinctive visual preference profiles of 13 subjects were
learned under different weather conditions, demonstrating the feasibility of
the learning framework. Entropy of the distribution of the most preferred
visual condition is computed for each learned preference profile to quantify
the certainty. Learning speed varies with subjects, but using a single variable
model (vertical illuminance on the eye), most subjects could be learned to an
acceptable certainty level within one day of stable weather, which shows the
efficiency of the method (learning outcomes). </p>
<p>Finally, a personalized shading control
framework is developed to maximize occupant satisfaction while minimizing lighting
energy use in daylit offices with roller shades. An integrated
lighting-daylighting simulation model is used to predict lighting energy use
while it also provides inputs for computing personalized visual preference
profiles, previously developed using Bayesian inference from comparative
preference data. The satisfaction utility and the predicted lighting energy use
are then used to form an optimization framework. We demonstrate the results of:
(i) a single objective formulation, where the satisfaction utility is simply
used as a constraint to when minimizing lighting energy use and (ii) a
multi-objective optimization scheme, where the satisfaction utility and
predicted lighting energy use are formulated as parallel objectives. Unlike
previous studies, we present a novel way to apply the MOO without assigning
arbitrary weights to objectives: allowing occupants to be the final decision
makers in real-time balancing between their personalized visual satisfaction
and energy use considerations, within dynamic hidden optimal bounds – through a
simple interface. </p>
<p>In summary, we present the first
method to incorporate personalized visual preferences in optimal daylighting
control, with energy use considerations, without using generic occupant
behavior models or discomfort-based assumptions.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/11316296 |
| Date | 04 December 2019 |
| Creators | Jie Xiong (8079911) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/AN_ADAPTIVE_PERSONALIZED_DAYLIGHTING_CONTROL_APPROACH_FOR_OPTIMAL_VISUAL_SATISFACTION_AND_LIGHTING_ENERGY_USE_IN_OFFICES/11316296 |
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