SEN700 Research Methodology
Pass Task 5.1P-C-D-HD: Annotated Bibliography
Submission Template
Topic of
Interest
Smart HVAC Control Systems for Energy Savings in Green Buildings: A
Your
researc
h
questio
n
Identify a research question (doesn’t have to be right) to guide your selection
of relevant articles.
Keywor
ds
1: Smart HVAC control systems
Performance Analysis
How can advanced control strategies, including IoT integration, occupancybased systems, and predictive modelling, improve the energy efficiency and
operational performance of HVAC systems in commercial and residential
buildings?
2: HVAC Energy saving
3: HVAC control system
4: Smart Building
5: HVAC automation in green buildings
(use red colour text for the keywords that have changed from previous
submission)
Databas
es
searche
d
1: ScienceDirect
Search
Term
Database 1: (keywords & Boolean operators)
2: IEEE Explore
3. MDPI
1.1 HVAC and Controls
1.2 HVAC and Energy Saving
Database 2: (keywords & Boolean operators)
2.1 HVAC Controls
2.2 HVAC and Smart Building
Database 3: (keywords & Boolean operators)
3.1 HVAC Control System
Annotated bibliography (prepare annotations for 5 articles for a pass, 8 for credit, 12 for
distinction, 15 for high distinction)
Article 1
Cong, G., Wang, R., & Wang, D. (2024, 22-24 Nov. 2024). Application of Internet of
Things in Distributed HVAC Control. 2024 11th International Forum on
Electrical Engineering and Automation (IFEEA),
This conference paper aims to demonstrate how Internet of Things (IoT)
technologies can be applied to distributed HVAC control systems to improve
system responsiveness, efficiency, and automation. The key research question
focuses on how IoT-based distributed control architecture enhances data
collection, communication, and decision-making in HVAC environments. The
authors used a prototype-based experimental method, where an IoT framework
was developed and tested to control HVAC components across distributed zones.
Data was collected from sensors and processed via embedded controllers to
optimize heating and cooling operations in real time. The key finding indicates that
distributed IoT control significantly improves system flexibility, scalability, and
energy performance compared to centralized control. The authors evaluated the
framework based on its responsiveness and ability to manage multiple zones
effectively. One limitation of the study is that it was conducted in a simulated or
controlled environment, which may not fully reflect the complexities of real-world
building operations; this is inferred from the scope of the experimental setup.
From this paper, I learned how distributed IoT architectures can decentralize
decision-making in HVAC systems, which is especially beneficial in large or
complex buildings. The authors suggest future work should include real-world
deployment, system integration challenges, and further development of intelligent
control algorithms to enhance adaptability and performance.
Article 2
Homod, R. Z., Gaeid, K. S., Dawood, S. M., Hatami, A., & Sahari, K. S. (2020).
Evaluation of energy-saving potential for optimal time response of HVAC
control system in smart buildings. Applied Energy, 271, 115255.
https://doi.org/https://doi.org/10.1016/j.apenergy.2020.115255
This study aims to evaluate how optimizing the time response of HVAC control
systems can contribute to energy savings in smart building environments. The
central research question explores how time delay settings affect HVAC
performance and energy consumption. The authors employed a simulation-based
approach using the EnergyPlus modelling tool, with calibration based on real
operational data. Multiple scenarios were tested to examine how varying response
times influence overall energy usage and indoor thermal comfort. The key findings
demonstrate that minimizing time delays in control systems can yield significant
energy savings, especially during peak periods, without negatively impacting
comfort. Results were evaluated through comparative energy consumption across
scenarios and validated using performance metrics from simulation outputs. A
noted limitation is the study's dependence on simulations, which, while useful,
may not fully capture the complexity of human behaviour, equipment degradation,
or environmental variability in real-world settings. This limitation is partially
acknowledged by the authors. From this article, I learned that control
responsiveness is a crucial but often overlooked variable in HVAC energy
optimization strategies. The authors propose that future work should focus on
real-world implementation, adaptive algorithms, and testing under varied building
types and climates to enhance system generalizability and performance.
Article 3
Huang, G., Ng, S. T., Li, D., & Zhang, Y. (2024). State of the art review on the HVAC
occupant-centric control in different commercial buildings. Journal of
Building Engineering, 96, 110445.
https://doi.org/https://doi.org/10.1016/j.jobe.2024.110445
This article aims to provide a comprehensive overview of occupant-centric control
(OCC) strategies in HVAC systems across various commercial building types. The
authors investigate the current state of OCC research to determine how occupant
preferences, behaviour, and presence influence HVAC control strategies. Through
a structured literature review, they analyse over 150 peer-reviewed publications
and categorize the control approaches into rule-based, model-based, and datadriven systems. The paper highlights how integrating occupant data into HVAC
operations can enhance thermal comfort and reduce energy consumption. Key
findings show that data-driven OCC methods—particularly those using machine
learning and sensor networks—offer greater adaptability and performance in
dynamic building environments. Evaluation was performed by comparing
methodologies, performance metrics, and reported benefits across studies. A
limitation noted by the authors is the lack of standardization and the difficulty in
generalizing OCC strategies across different climates and building types due to
variable occupant behaviour. From this review, I learned that occupant-centric
HVAC control is gaining traction as a method to improve building energy efficiency
while maintaining comfort, but challenges remain in scalability and integration.
The authors recommend future research focus on hybrid models, improved
occupant sensing technologies, and real-world validations to enhance OCC
implementation across commercial sectors.
Article 4
Markus, A., Bursill, J., Gunay, B., & Hobson, B. (2024). Projecting and estimating
HVAC energy savings from correcting control faults: Comparison between
physical and virtual metering approaches. Energy and Buildings, 328,
115169. https://doi.org/10.1016/j.enbuild.2024.115169
This study aims to compare the accuracy and practicality of physical and virtual
metering approaches in estimating HVAC energy savings achieved through the
correction of control faults. The research questions center on how reliably virtual
metering can replicate savings estimates compared to more traditional physical
submetering methods. The authors employed a case study-based methodology,
analyzing data from several commercial buildings where HVAC control faults were
identified and corrected. They used both physical energy meters and calibrated
virtual models to estimate energy savings, then compared the results across
multiple scenarios. The key findings reveal that virtual metering can closely
approximate physical measurements, with differences typically within an
acceptable range (10–15%), making it a cost-effective and scalable alternative for
performance monitoring. Evaluation was performed by validating virtual estimates
against physical meter readings across various timeframes. One limitation is that
virtual metering accuracy depends heavily on model calibration and input data
quality, which may vary significantly between buildings. This is acknowledged by
the authors. From this article, I learned that virtual metering offers promising
potential for scaling energy monitoring efforts, especially in buildings lacking
submetering infrastructure. The authors suggest future research should focus on
improving model robustness, automating calibration processes, and expanding
case studies to include diverse building types and fault scenarios.
Mistry, V. (2023). The Role of IoT in Enhancing HVAC Control Systems. Journal of
Biosensors and Bioelectronics Research, 1, 1-5.
(Minimu
https://doi.org/10.47363/JBBER/2023(1)115
Article 5
m 5 for
a Pass)
Article 6
This paper explores how integrating Internet of Things (IoT) technologies can
improve the performance of HVAC systems by enabling centralized control,
predictive maintenance, and greater energy efficiency. The research questions
focus on identifying how IoT can be applied to optimize HVAC operations and what
specific benefits it offers in terms of system responsiveness and energy savings.
The authors conducted a literature review, analyzing existing studies and
applications of smart sensors, automation, and cloud computing in building
management systems. Their findings suggest that IoT-enabled HVAC systems
allow for real-time monitoring and fault detection, which improves operational
efficiency and reduces energy consumption. These conclusions are based on a
synthesis of previously reported outcomes and case studies rather than new
experimental data. One limitation of the paper is its lack of empirical analysis or
real-world implementation results; this is inferred from the absence of detailed
methodology or collected data. Despite this, the paper effectively highlights the
potential of IoT integration in modernizing HVAC systems. It underscores the need
for future work to focus on real-world testing, cybersecurity challenges, and costbenefit evaluations to support broader adoption. This study provides a strong
conceptual foundation for researchers and professionals interested in smart
building technologies and energy-efficient HVAC solutions.
Pang, Z., Guo, M., Smith-Cortez, B., O'Neill, Z., Yang, Z., Liu, M., & Dong, B. (2024).
Quantification of HVAC energy savings through occupancy presence
sensors in an apartment setting: Field testing and inverse modeling
approach. Energy and Buildings, 302, 113752.
https://doi.org/https://doi.org/10.1016/j.enbuild.2023.113752
This study aims to quantify the energy-saving potential of HVAC systems equipped
with occupancy presence sensors in a residential apartment setting. The main
research question focuses on how presence-based control affects energy use
compared to conventional time-scheduled HVAC operation. The authors
conducted field experiments over a two-month period in an apartment unit,
integrating motion sensors to activate or deactivate the HVAC system based on
real-time occupancy. They used an inverse modelling approach to estimate the
baseline energy usage and compared it with the actual consumption under
occupancy-based control. The findings show that using presence sensors can
lead to significant HVAC energy savings—approximately 21%—without sacrificing
indoor comfort. Results were evaluated by comparing measured energy use with
the modelled baseline, offering a robust method for quantifying savings. A key
limitation of the study is its single-unit focus, which may not fully represent the
variability across different household types or climate zones; this limitation is
acknowledged by the authors. From this research, I learned that integrating
occupancy sensing into HVAC control offers a practical and effective means of
reducing residential energy consumption. The authors recommend future work to
extend this study across multiple units and explore integration with more
advanced control algorithms to enhance energy efficiency on a broader scale.
Article 7
Pereira Pinheiro, L., Guo, M., Pang, Z., & O’Neill, Z. (2025). Quantifying Energy
Savings of Occupancy- Centric HVAC Controls Utilizing Longitudinal
Occupancy Sensing Data and Calibrated Energy Simulations.
This paper aims to quantify the energy savings potential of occupancy-centric
HVAC control strategies using long-term occupancy data and energy simulations
calibrated with real building data. The primary research question explores how
detailed occupancy sensing can inform HVAC control to reduce energy
consumption without compromising comfort. The authors use longitudinal
occupancy data collected over two years from a university building and apply it to
energy simulation models using EnergyPlus. They calibrate the models based on
real energy use data to ensure accuracy in comparing baseline and occupancybased scenarios. Key findings show that implementing occupancy-centric control
strategies can reduce HVAC energy use by up to 24%, especially during off-peak
hours and in underutilized spaces. Evaluation involved comparing energy usage
patterns between conventional and occupancy-based models while maintaining
thermal comfort standards. One limitation of the study is its focus on a single
building and institutional context, which may affect the generalizability of the
results to other building types or climates. This limitation is acknowledged by the
authors. From this study, I learned that detailed occupancy monitoring combined
with simulation-based analysis can effectively support energy-saving decisions in
building operations. The authors suggest future research should expand to multibuilding studies, incorporate adaptive comfort models, and explore integration
with real-time building management systems.
Article 8
Petrie, C., Gupta, S., Rao, V., & Nutter, B. (2018, 4-6 April 2018). Energy Efficient
Control Methods of HVAC Systems for Smart Campus. 2018 IEEE Green
Technologies Conference (GreenTech),
(minimu
m 8 for
Credit)
This paper aims to investigate energy-efficient control strategies for HVAC
systems in smart campus environments by analyzing various control methods that
improve energy savings without compromising indoor comfort. The central
research question explores which control methods are most effective in reducing
HVAC-related energy consumption across large-scale university buildings. The
authors present a case study conducted at Texas Tech University, where multiple
buildings with different HVAC control setups were analyzed. Data were collected
from building management systems, including HVAC runtime, zone temperature,
and occupancy schedules. The study compares constant volume systems with
advanced variable air volume (VAV) and demand-controlled ventilation (DCV)
strategies. Key findings reveal that HVAC systems with adaptive controls such as
DCV can reduce energy consumption by up to 40% compared to fixed control
systems, especially in buildings with variable occupancy. Evaluation was based on
energy usage data and control responsiveness over the course of the study. One
limitation is that the study is specific to the Texas climate and infrastructure,
which may not be directly applicable to other regions or campuses with different
HVAC technologies; this is noted by the authors. From this paper, I learned how
tailored control strategies and data-informed decision-making can significantly
improve energy efficiency in educational facilities. The authors suggest future
work should explore integration with renewable energy sources and machine
learning algorithms to further optimize smart campus HVAC performance.
Article 9
Srisurapanon, P., & Banjerdpongchai, D. (2023, 17-20 Oct. 2023). Design of
Supervisory Model Predictive Control for HVAC Systems and Two Zones
with Consideration of Energy Efficiency and Thermal Comfort. 2023 23rd
International Conference on Control, Automation and Systems (ICCAS),
This paper aims to develop a supervisory model predictive control (MPC) strategy
for HVAC systems that optimizes both energy efficiency and thermal comfort
across two independent building zones. The research questions focus on how
MPC can be used to manage multi-zone HVAC systems effectively, and what
trade-offs exist between comfort and energy savings. The authors use a
simulation-based approach, constructing a state-space model of a two-zone
building and applying an MPC algorithm that dynamically adjusts setpoints based
on occupancy and external conditions. The model is designed to minimize energy
consumption while maintaining comfort within defined temperature bounds. Key
findings demonstrate that the proposed MPC strategy reduces energy usage more
effectively than traditional rule-based control, particularly by adapting to zonespecific requirements. The simulation results show clear advantages in both
energy reduction and thermal comfort indices. A limitation of the study is its
simulation-only nature, which does not yet reflect the complexities of real-world
HVAC systems such as sensor noise, equipment delays, or occupant behaviour;
this is acknowledged by the authors. From this paper, I learned that model
predictive control offers a promising method for balancing competing objectives
in HVAC management. The authors recommend future research include real-time
implementation and testing in physical buildings to validate the model’s practical
performance.
Article
10
Tang, L., Xie, H., Wang, Y., & Xu, Z. (2025). Deeply flexible commercial building
HVAC system control: A physics-aware deep learning-embedded MPC
approach. Applied Energy, 388, 125631.
https://doi.org/https://doi.org/10.1016/j.apenergy.2025.125631
This study aims to improve the flexibility and efficiency of HVAC control in
commercial buildings by proposing a novel model predictive control (MPC)
framework that incorporates physics-based modelling with deep learning
techniques. The central research question explores how combining data-driven
models with physical constraints can enhance HVAC system control performance
under varying building conditions. The authors develop a physics-aware deep
neural network that learns thermal dynamics while respecting first-principle laws
and embed this model into an MPC framework to optimize HVAC operations.
Simulations are conducted using real-world data from a commercial building to
compare the proposed approach against conventional MPC and rule-based
methods. Key findings reveal that the deep learning-embedded MPC approach
significantly improves control flexibility, reduces energy use, and maintains
thermal comfort more consistently across different conditions. Evaluation metrics
included energy consumption, comfort violation percentages, and computational
efficiency. A limitation of the study is its simulation-based validation, which may
not account for real-time disturbances or hardware limitations in practice; this
limitation is acknowledged by the authors. From this paper, I learned that
hybridizing physical modelling with deep learning can create more adaptable and
accurate control systems for building energy management. The authors
recommend future research focus on real-world implementation and broader
generalization across building types and climates to validate the scalability of the
proposed method.
Article
11
Wang, X., & Dong, B. (2024). Long-term experimental evaluation and comparison
of advanced controls for HVAC systems. Applied Energy, 371.
https://doi.org/10.1016/j.apenergy.2024.123706
This study aims to experimentally evaluate and compare the long-term
performance of advanced HVAC control strategies, including rule-based, model
predictive control (MPC), and reinforcement learning-based methods. The main
research question investigates how different advanced control algorithms perform
in terms of energy savings and occupant comfort over an extended operational
period. The authors conducted a year-long field experiment in a university office
building equipped with sensors, control systems, and data logging infrastructure.
Each control strategy was deployed and monitored for several months to allow for
fair comparison. The key findings demonstrate that MPC and reinforcement
learning-based controls achieved notable energy savings—up to 29%—while
maintaining or improving thermal comfort compared to traditional rule-based
control. The evaluation considered both quantitative energy consumption data
and qualitative comfort feedback from occupants. A limitation of the study is its
single-building test environment and specific HVAC configuration, which may limit
the generalizability of the findings; this is acknowledged by the authors. From this
paper, I learned how advanced, data-driven HVAC control strategies can deliver
consistent performance improvements over traditional methods when applied in
real-world settings. The authors recommend future work focus on scaling these
approaches to larger building portfolios and integrating them with building
automation systems to support broader adoption.
Article
12
Zhang, X., Shi, W., Li, X., Yan, B., Malkawi, A., & Li, N. (2016, 7-9 Dec. 2016).
Decentralized temperature control via HVAC systems in energy efficient
buildings: An approximate solution procedure. 2016 IEEE Global
Conference on Signal and Information Processing (GlobalSIP),
(minimu
m 12 for
Distincti This paper aims to develop a decentralized control approach for HVAC systems in
on)
energy-efficient buildings, focusing on optimizing temperature regulation while
addressing computational complexity. The primary research question is how to
effectively implement scalable and efficient decentralized temperature control
using HVAC systems without compromising occupant comfort or energy savings.
The authors propose a novel algorithm that approximates the optimal solution of a
centralized temperature control problem using dual decomposition and iterative
methods. The method allows each zone in a building to independently adjust its
HVAC output based on local information, while still coordinating with a central
objective. Simulations were conducted on a multi-zone building model to evaluate
control accuracy, convergence speed, and energy efficiency. Key findings show
that the decentralized approach closely approximates centralized control
performance with significantly lower computation time, making it suitable for
large-scale applications. Evaluation metrics included energy consumption,
thermal comfort deviation, and computational efficiency. A limitation of the study
is its reliance on ideal communication conditions between zones, which may not
hold in practice; this is acknowledged by the authors. From this paper, I learned
how decentralized algorithms can make advanced HVAC control more feasible for
complex buildings by reducing computation burdens while maintaining
performance. The authors suggest future work should explore real-world testing
and integration with sensor networks to validate practical deployment.
Article
13
Citation: Use EndNote “Cite while you write” function to cite the article using a
consistent referencing style. APA7th, Author Date, IEEE, Harvard styles are all
acceptable.
Aim: In your own words (and in no more than 75 words) describe the aim of
the research presented in the article. What did the authors set out to do?
Research question(s): Identify the research question(s) and capture that here
Methodology and methods: Identify the research methodology used and the
associated methods for data collection and analysis
Key findings: What were the main findings?
Limitation: What were the limitations identified by the authors?
Reflection: What you think about the relevance of this research for your own
research topic of interest? What was good, what is missing? What did you
learn?
Article
14
Citation: Use EndNote “Cite while you write” function to cite the article using a
consistent referencing style. APA7th, Author Date, IEEE, Harvard styles are all
acceptable.
Aim: In your own words (and in no more than 75 words) describe the aim of
the research presented in the article. What did the authors set out to do?
Research question(s): Identify the research question(s) and capture that here
Methodology and methods: Identify the research methodology used and the
associated methods for data collection and analysis
Key findings: What were the main findings?
Limitation: What were the limitations identified by the authors?
Reflection: What you think about the relevance of this research for your own
research topic of interest? What was good, what is missing? What did you
learn?
Article
15
Citation: Use EndNote “Cite while you write” function to cite the article using a
consistent referencing style. APA7th, Author Date, IEEE, Harvard styles are all
(Minimu acceptable.
m 15 for Aim: In your own words (and in no more than 75 words) describe the aim of
High
the research presented in the article. What did the authors set out to do?
Distincti Research question(s): Identify the research question(s) and capture that here
on)
Methodology and methods: Identify the research methodology used and the
associated methods for data collection and analysis
Key findings: What were the main findings?
Limitation: What were the limitations identified by the authors?
Reflection: What you think about the relevance of this research for your own
research topic of interest? What was good, what is missing? What did you
learn?
EndNot
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