<p>Commercial buildings not only have significant
impacts on occupants’ well-being, but also contribute to more than 19% of the total
energy consumption in the United States. Along with improvements in building
equipment efficiency and utilization of renewable energy, there has been significant
focus on the development of advanced heating, ventilation, and air conditioning (HVAC) system controllers that incorporate
predictions (e.g., occupancy patterns, weather forecasts) and current state
information to execute optimization-based strategies. For example, model predictive
control (MPC) provides a systematic implementation option using a system model
and an optimization algorithm to adjust the control setpoints dynamically. This
approach automatically satisfies component and operation constraints related to
building dynamics, HVAC equipment, etc. However, the wide adaptation of advanced
controls still faces several practical challenges: such approaches
involve significant engineering effort and require site-specific solutions for
complex problems that need to consider uncertain weather forecast and engaging
the building occupants. This thesis explores smart building operation
strategies to resolve such issues from the following three aspects. </p>
<p>First, the thesis explores a stochastic
model predictive control (SMPC) method for the optimal utilization of solar
energy in buildings with integrated solar systems. This approach considers the
uncertainty in solar irradiance forecast over a prediction horizon, using a new
probabilistic time series autoregressive model, calibrated on the sky-cover
forecast from a weather service provider. In the optimal control formulation,
we model the effect of solar irradiance as non-Gaussian stochastic disturbance
affecting the cost and constraints, and the nonconvex cost function is an
expectation over the stochastic process. To solve this optimization problem, we
introduce a new approximate dynamic programming methodology that represents the
optimal cost-to-go functions using Gaussian process, and achieves good solution
quality. We use an emulator to evaluate the closed-loop operation of a
building-integrated system with a solar-assisted heat pump coupled with radiant
floor heating. For the system and climate considered, the SMPC saves up to 44%
of the electricity consumption for heating in a winter month, compared to a
well-tuned rule-based controller, and it is robust, imposing less uncertainty
on thermal comfort violation.</p>
<p>Second,
this thesis explores user-interactive thermal environment control systems that
aim to increase energy efficiency and occupant satisfaction in office
buildings. Towards this goal, we present a new modeling approach of occupant
interactions with a temperature control and energy use interface based on
utility theory that reveals causal effects in the human decision-making process.
The model is a utility function that quantifies occupants’ preference over
temperature setpoints incorporating their comfort and energy use
considerations. We demonstrate our approach by implementing the
user-interactive system in actual office spaces with an energy efficient model
predictive HVAC controller. The results show that with the developed
interactive system occupants achieved the same level of overall satisfaction
with selected setpoints that are closer to temperatures determined by the MPC
strategy to reduce energy use. Also, occupants often accept the default MPC
setpoints when a significant improvement in the thermal environment conditions
is not needed to satisfy their preference. Our results show that the occupants’
overrides can contribute up to 55% of the HVAC energy consumption on average
with MPC. The prototype user-interactive system recovered 36% of this
additional energy consumption while achieving the same overall occupant satisfaction
level. Based on these findings, we propose that the utility model can become a
generalized approach to evaluate the design of similar user-interactive systems
for different office layouts and building operation scenarios. </p>
<p>Finally, this thesis presents an
approach based on meta-reinforcement learning (Meta-RL) that enables autonomous
optimal building controls with minimum engineering effort. In reinforcement
learning (RL), the controller acts as an agent that executes control actions in
response to the real-time building system status and exogenous disturbances according
to a policy. The agent has the ability to update the policy towards improving
the energy efficiency and occupant satisfaction based on the previously
achieved control performance. In order to ensure satisfactory performance upon
deployment to a target building, the agent is trained using the Meta-RL
algorithm beforehand with a model universe obtained from available building
information, which is a probability measure over the possible building
dynamical models. Starting from what is learned in the training process, the
agent then fine-tunes the policy to adapt to the target building based on-site
observations. The control performance and adaptability of the Meta-RL agent is
evaluated using an emulator of a private office space over 3 summer months. For
the system and climate under consideration, the Meta-RL agent can successfully
maintain the indoor air temperature within the first week, and result in only
16% higher energy consumption in the 3<sup>rd</sup> month than MPC, which
serves as the theoretical upper performance bound. It also significantly
outperforms the agents trained with conventional RL approach. </p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/13296617 |
| Date | 15 December 2020 |
| Creators | Xiaoqi Liu (9681032) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY-NC-SA 4.0 |
| Relation | https://figshare.com/articles/thesis/Exploration_of_Intelligent_HVAC_Operation_Strategies_for_Office_Buildings/13296617 |
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