Heating, ventilation, and air conditioning (HVAC)[1] is the use of various technologies to control the temperature, humidity, and purity of the air in an enclosed space. Its goal is to provide thermal comfort and acceptable indoor air quality. HVAC system design is a subdiscipline of mechanical engineering, based on the principles of thermodynamics, fluid mechanics, and heat transfer. "Refrigeration" is sometimes added to the field's abbreviation as HVAC&R or HVACR, or "ventilation" is dropped, as in HACR (as in the designation of HACR-rated circuit breakers).

1. 1. Aim and Objectives
The aim of this thesis is to develop models and optimization techniques for ACMV systems in smart
buildings to enhance energy efficiency and sustainability while maintaining indoor comfort and air quality.
Objectives
1.Model Development:
• Develop mathematical and computational models to simulate the behavior of ACMV systems, including
HVAC (Heating, Ventilation, and Air Conditioning) equipment, control systems, and building envelope
dynamics.
• Incorporate factors such as building geometry, occupancy patterns, weather conditions, and thermal
properties of materials into the models.
2.Performance Evaluation:
• Assess the energy consumption and performance of existing ACMV systems in smart buildings through
data collection and analysis.
• Identify inefficiencies, areas for improvement, and opportunities for energy savings in the operation of
ACMV systems.

2. 3. Optimization Techniques:
• Investigate optimization algorithms and strategies for improving the energy efficiency of ACMV systems.
• Develop algorithms for optimal control of HVAC equipment, including scheduling, setpoint optimization,
and demand response strategies.
• Explore advanced control techniques such as model predictive control (MPC) and reinforcement learning
for adaptive and responsive operation of ACMV systems.
4. Integration with Building Automation Systems (BAS):
• Integrate ACMV optimization techniques with building automation and management systems to create a
holistic approach to energy management.
• Develop communication protocols and interfaces for real-time monitoring, control, and coordination of
ACMV systems with other building systems and utilities.
5.Indoor Environmental Quality (IEQ) Considerations:
• Consider the impact of ACMV optimization on indoor air quality, thermal comfort, and occupant
satisfaction.
• Develop optimization strategies that prioritize energy efficiency while maintaining acceptable indoor
environmental conditions and complying with relevant standards and guidelines.

3. 6. Life Cycle Cost Analysis:
• Perform life cycle cost analysis to evaluate the economic feasibility and benefits of implementing optimized
ACMV systems.
• Estimate initial investment costs, energy savings, maintenance expenses, and lifecycle performance to
assess the return on investment and payback period.
7. Demonstration and Validation:
• Implement and validate the developed models and optimization techniques through simulation studies and
real-world experiments in smart building testbeds or pilot projects.
• Evaluate the effectiveness and scalability of the proposed solutions across different building types,
climates, and operational scenarios.
8. Knowledge Dissemination and Outreach:
• Disseminate research findings through publications in academic journals, conference presentations, and
industry reports.
• Engage with stakeholders, building owners, operators, and policymakers to promote the adoption of
energy-efficient ACMV solutions and support sustainable building practices.

4. The research aims to advance the state-of-the-art in modeling and optimization of ACMV systems for
energy-efficient smart buildings, contributing to the development of sustainable and resilient built environments.
The objectives of this thesis can be divided into the following three steps:
• Phase 1: Modeling energy consumption of ACMV systems with machine learning data-driven approaches.
• Phase 2: Modeling indoor thermal comfort sensations of occupants with machine learning passive and active
approaches. The passive approach is mainly based on environmental parameters, while the active approach
directly focuses on physiological parameters of occupants.
• Phase 3: Formulating problems and optimizing on enhancing smart buildings’ energy efficiency and maintaining
indoor thermal comfort sensations of occupant.
2. Scope of Thesis
The scope of a thesis on modeling and optimization of Air Conditioning and Mechanical Ventilation
(ACMV) systems for energy-efficient smart buildings can be comprehensive, encompassing various aspects
related to building energy management, HVAC system operation, optimization techniques, and smart building
technologies.

5. 3. Implementation of the Program
Implementation of a program focused on modeling and optimizing Air Conditioning and Mechanical
Ventilation (ACMV) systems for energy-efficient smart buildings:
1. Program Introduction
•Overview of the program's objectives and scope
•Importance of optimizing ACMV systems for energy-efficient smart buildings
•Explanation of the approach and methodologies utilized in the program
2. Data Collection and System Characterization
•Gather data on building characteristics, including geometry, occupancy patterns, and thermal properties
•Collect weather data and other external factors influencing building energy consumption
•Characterize existing ACMV systems, including equipment specifications, control settings, and performance
metrics
3. Development of Mathematical Models
•Develop mathematical models to represent the behavior of ACMV systems and their interactions with the
building environment

6. •Include models for HVAC equipment, building envelope dynamics, control systems, and occupant behavior
•Validate models using real-world data and empirical observations
4. Simulation Platform Setup
•Set up a simulation platform or software environment for modeling and optimization purposes
•Configure simulation tools to integrate with building energy management systems and other relevant
software platforms
•Ensure compatibility with optimization algorithms and performance assessment metrics
5. Optimization Algorithm Design
•Design optimization algorithms tailored to ACMV system operation and energy efficiency objectives
•Explore optimization techniques such as mathematical programming, genetic algorithms, or machine
learning- based approaches
•Define optimization objectives, constraints, and decision variables based on energy consumption, indoor
comfort, and other relevant factors
6. Integration with Building Automation Systems (BAS)
•Integrate optimization algorithms with building automation systems for real-time monitoring and control of
ACMV systems

7. •Develop interfaces and protocols for communication between optimization software and BAS platforms
•Implement strategies for seamless coordination and interoperability between ACMV optimization and other
building systems
7. Implementation and Testing
•Implement the developed optimization program in a testbed or pilot smart building environment
•Conduct comprehensive testing and validation to assess the effectiveness and performance of the
optimization algorithms
•Evaluate energy savings, indoor comfort improvements, and other key performance indicators compared to
baseline conditions
8. Performance Monitoring and Feedback
•Establish mechanisms for continuous performance monitoring and feedback loop between simulation results
and real-world operation
•Collect and analyze data on energy consumption, indoor environmental quality, and occupant satisfaction
•Use feedback to refine optimization algorithms and improve system performance over time

8. 9. Documentation and Reporting
•Document the implementation process, including software configurations, simulation setup, and optimization
results
•Prepare technical reports, presentations, and publications to disseminate findings to stakeholders,
researchers, and practitioners
•Share lessons learned, best practices, and recommendations for future implementations
10. Conclusion and Future Directions
•Summarize key findings and achievements of the implementation program
•Discuss implications for energy-efficient smart building design, operation, and policy
•Identify opportunities for further research, development, and application of ACMV optimization technologies
Researchers and practitioners can effectively model, optimize, and implement Air Conditioning and
Mechanical Ventilation (ACMV) systems to enhance energy efficiency and sustainability in smart buildings.

9. 4. Outline of the Thesis
This thesis is composed of Five chapters
Chapter 1 deals with introduction.
Chapter 2 Literature review
Chapter 3 Methodology - Modeling/Optimization of Energy Consumption and Thermal Comfort
Chapter 4 Energy Efficiency Evaluation - Using Passive Approaches
Chapter 5 Energy Efficiency Evaluation - Using Active Approaches
Chapter 6 consists of discussion, conclusions and future directions of the thesis.