<p>The residential sector is responsible for
approximately 21% of the total energy use in the U.S. As a result, there have
been various programs and studies aiming to reduce energy consumption and
utility burden on individual households. Among various energy efficiency
strategies, behavior-based approaches have received considerable attention
because they significantly affect operational energy consumption without requiring
building upgrades. For example, up to 30% of heating and cooling energy savings
can be achieved by having an efficient temperature setpoint schedule. Such
approaches can be particularly beneficial for multi-family residential
buildings because 88% of their residents are renters paying their own utility
bills without being allowed to upgrade their housing unit.</p>
<p>In this context, eco-feedback has emerged as an
approach to motivate residents to reduce energy use by providing information
(feedback) on human behavior and environmental impact. This research has gained
significant attention with the development of new smart home technology such as
smart thermostats and home energy management systems. Research on the design of
effective eco-feedback focuses on how to motivate residents to change their
behavior by identifying and notifying implementable actions in a timely manner
via energy analytics such as energy prediction models, energy disaggregation,
etc.</p>
<p>However, unit-level energy analytics pose significant
challenges in multi-family residential buildings tasks due to the inter-unit
heat transfer, unobserved variables (e.g., infiltration, human body heat gain,
etc.), and limited data availability from the existing infrastructure (i.e.,
smart thermostats and smart meters). Furthermore, real-time model inference can
facilitate up-to-date eco-feedback without a whole year of data to train
models. To tackle the aforementioned challenges, three new modeling approaches
for energy analytics have been proposed in this Thesis is developed based on
the data collected from WiFi-enabled smart thermostats and power meters in a multi-family
residential building in IN, U.S.</p>
<p>First, this Thesis presents a unit-level data-driven
modeling approach to normalize heating and cooling (HC) energy usage in
multi-family residential buildings. The proposed modeling approach provides
normalized groups of units that have similar building characteristics to
provide the relative evaluation of energy-related behaviors. The
physics-informed approach begins from a heat balance equation to derive a
linear regression model, and a Bayesian mixture model is used to identify
normalized groups in consideration of the inter-unit heat transfer and
unobserved variables. The probabilistic
approach incorporates unit- and season-specific prior information and
sequential Bayesian updating of model parameters when new data is available.
The model finds distinct normalized HC energy use groups in different seasons
and provides more accurate rankings compared to the case without normalization.</p>
<p>Second, this Thesis presents a real-time modeling
approach to predict the HC energy consumption of individual units in a
multi-family residential building. The model has a state-space structure to
capture the building thermal dynamics, includes the setpoint schedule as an
input, and incorporates real-time state filtering and parameter learning to
consider uncertainties from unobserved boundary conditions (e.g., temperatures
of adjacent spaces) and unobserved disturbances (i.e., window opening,
infiltration, etc.). Through this real-time form, the model does not need to be
re-trained for different seasons. The results show that the median power
prediction of the model deviates less than 3.1% from measurements while the
model learns seasonal parameters such as the cooling efficiency coefficient
through sequential Bayesian update.</p>
Finally, this Thesis presents
a scalable and practical HC energy disaggregation model that is designed to be developed
using data from smart meters and smart thermostats available in current advanced
metering infrastructure (AMI) in typical residential houses without additional
sensors. The model incorporates sequential Bayesian update whenever a new
operation type is observed to learn seasonal parameters without long-term data
for training. Also, it allows modeling the skewed characteristics of HC and
non-HC power data. The results show that the model successfully predicts
disaggregated HC power from 15-min interval data, and it shows less than 12% of
error in weekly HC energy consumption. Finally, the model is able to learn
seasonal parameters via sequential Bayesian update and gives good prediction
results in different seasons.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/15057141 |
| Date | 27 July 2021 |
| Creators | Sang Woo Ham (11185884) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Energy_Analytics_for_Eco-feedback_Design_in_Multi-family_Residential_Buildings/15057141 |
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