| Journal of Collective Sciences and Sustainability
Received: 05 May 2026; Revised: 16 September 2026; Accepted: 06 October 2026; Published Online: 08 October 2026.
J. Collect. Sci. Sustain., 2026, 2(4), 26414| Volume 2 Issue 4 (December 2026) | DOI: https://doi.org/10.64189/css.26414
© The Author(s) 2026
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0).
HVAC Framework for Buildings: A Stage-Gated
Delivery and Performance Assurance Approach
Amey S. Wagh,
*
Naresh Anabathula, Saud Sakharkar, Anas Shaikh Sharif and Mohammed Anas Bijapure
Department of Mechanical Engineering, M.H. Saboo Siddik College of Engineering, Mumbai, Maharashtra, 400008, India
*Email: amey.wagh@mhssce.ac.in(Amey S. Wagh)
Abstract
The planning and implementation of heating, ventilation, and air conditioning (HVAC) systems are critical to
building energy efficiency, occupant comfort, and effective project management. Despite technological
advances in this field, many HVAC projects continue to face challenges related to inadequate project
coordination, planning deficiencies, and insufficient commissioning. These issues can lead to excessive energy
consumption, reduced system efficiency, and increased project costs. To address these challenges, this study
proposes a structured HVAC delivery and performance assurance framework that defines the project
workflow from planning through final system acceptance. The framework is divided into four phases:
initiation and analysis, detailed engineering, execution, commissioning and handover. The first phase involves
the systematic analysis of building and climatic information, together with heating and cooling load
calculations, to identify an appropriate HVAC system. The second phase focuses on the preparation of detailed
drawings, equipment selection, and design coordination. The third phase focuses on procurement, installation,
and quality control during construction. The final phase involves system testing, adjusting, balancing, and
performance verification to ensure that the system meets the specified performance requirements.
Keywords: Heating, Ventilation and Air Conditioning framework; Building services engineering; Commissioning
process; Energy efficiency; Sustainable building systems; Lifecycle performance.
1. Introduction
Heating, ventilation, and air conditioning (HVAC) systems are among the most energy-consuming systems in
residential and commercial buildings. According to the International Energy Agency, buildings account for a
large share of global energy use, and HVAC systems contribute significantly to this consumption. The American
Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Handbook indicates that heating
and cooling systems can represent nearly 40–60% of total building energy demand depending on climate and
building type.
[1]
In addition to energy use, HVAC systems directly affect indoor environmental quality (IEQ),
thermal comfort, occupant health, and productivity. The literature highlights that poor ventilation and
improper HVAC control can negatively impact occupant comfort and increase health-related symptoms.
[2]
Modern building automation and intelligent control systems can improve the indoor air quality and maintain
stable indoor conditions when properly designed and operated.
[3]
However, HVAC inefficiencies are not caused
only by equipment limitations. Many findings indicate that problems occur because of weak project planning,
a lack of coordination, and poor commissioning practices.
[4-6]
When architects, mechanical engineers,
contractors, and commissioning teams do not coordinate properly, a gap often occurs between the predicted
energy performance and actual building operation.
[7]
This finding shows that energy efficiency alone is not
sufficient; system performance must also consider human comfort and behavior. Ventilation losses are
another major issue. Orme reported that nearly 48% of heating energy can be lost because of air change
processes such as infiltration and uncontrolled ventilation.
[5]
This finding indicates that poor air sealing and
ventilation design can significantly reduce building energy efficiency. Furthermore, Mendell et al. reported
that compared with naturally ventilated buildings, mechanically ventilated office buildings presented a higher
prevalence of irritation and discomfort symptoms.
[2]
This highlights the importance of proper ventilation
design and system performance verification. Recent research has emphasized the role of intelligent systems,
predictive control, and digital technologies in improving HVAC performance.
[8]
High-performance operational
guidelines recommend structured control sequences and systematic verification to ensure system
reliability.
[9]
The results of previous studies indicate that HVAC performance gaps arise from both technical and
project management issues.
[7]
Many buildings fail to achieve their designed efficiency because of the absence
of a structured delivery framework that connects design, installation, and performance verification. Therefore,
a structured HVAC framework that integrates technical design, interdisciplinary coordination, commissioning
procedures, and performance benchmarking is needed to reduce energy waste, improve indoor comfort, and
ensure that actual system performance matches the original design intent.
[9,10]
While HVAC energy efficiency
and performance monitoring have been thoroughly investigated, most research focuses on individual
technologies, intelligent control strategies, or simulation models rather than structured project delivery
frameworks.
[4, 7,11]
Previous studies highlight the importance of system optimization and energy modeling;
however, they often do not integrate design verification, commissioning procedures, and operational
performance monitoring within a unified process.
[9,10]
Recent research further highlights that the building
energy-performance gap can result from interactions across the building lifecycle rather than from a single
technical cause.
[12]
As a result, a gap exists between the predicted HVAC performance during design and actual
building operation.
[7,12]
To address this gap, this study proposes a stage-gated HVAC performance assurance
framework that integrates technical verification, structured decision checkpoints, and lifecycle performance
monitoring to improve system reliability and energy efficiency.
2. Literature review
2.1 Intelligent control and automation
Recent literature highlights that advanced control methods using artificial intelligence (AI) and machine
learning (ML) can improve HVAC system efficiency and flexibility. Abida and Richter studied different neural
network models, such as CNN, LSTM, RNN-GRU, and attention-based methods for HVAC control
optimization.
[13]
Their review explained that these models can learn from historical building data and improve
temperature control and energy performance. However, they also state that these models must be tested on
different building types before large-scale implementation.
[4,13]
Ali and Aldaiyat developed an intelligent HVAC
control system based on support vector machine (SVM) algorithms for smart buildings.
[11]
Their system can
quickly adjust operating conditions to maintain indoor comfort. However, they reported that system accuracy
depends on good-quality data and fast response times.
[11]
Building automation systems (BASs) also plays an
important role in intelligent HVAC control. Advanced building operating system architectures have also been
proposed to integrate HVAC data streams, sensor networks, and control logic for improved system-level
optimization.
[14]
Mistry explained that automation systems monitor indoor air quality, temperature, and
humidity in real time and help improve system efficiency.
[3]
High-performance control sequences are also
recommended for reliable HVAC operation.
[9]
Grassi et al. reviewed more than 180 related studies and
concluded that HVAC control systems must balance energy savings with occupant comfort.
[15]
The authors
emphasized that user-friendly interfaces are necessary so that occupants can interact properly with the
system. From these studies, it is clear that intelligent HVAC systems can improve performance, but they
require proper commissioning, verified baseline data, and systematic validation procedures to operate
effectively.
[2,11]
2.2 Passive design and ventilation strategies
The literature highlights the importance of ventilation and passive design in reducing energy use and
improving occupant health. The British Standards Institution provides guidelines for natural ventilation
design in buildings.
[16]
These guidelines explain how building layout, window design, and airflow paths
influence ventilation effectiveness. Orme reported that nearly 48% of heating energy loss in buildings occurs
because of air change processes such as infiltration and ventilation.
[5]
This finding shows that uncontrolled air
leakage can significantly increase energy consumption. Mostafaeipour et al. studied wind catcher systems and
reported that they can reduce energy use and lower carbon emissions.
[17]
Yang validated computational fluid
dynamics (CFD) models for cross-ventilation analysis in naturally ventilated buildings.
[18]
Evola and Popov
compared turbulence models and reported that the RNG k-ε model predicts ventilation airflow more
accurately than the standard k-ε model does.
[19]
Mendell et al. reported that compared with naturally
ventilated buildings, mechanically ventilated office buildings presented greater irritation symptoms.
[2]
This
finding indicates that ventilation design affects occupant health and comfort. Siew et al. classified passive
design strategies for office buildings and reported that the proper use of natural ventilation and shading can
reduce energy consumption.
[20]
These studies confirm that proper building data collection, climate analysis,
and airflow modeling are essential in the early stage of HVAC planning.
[5,16,18]
2.3 Integrated energy systems and benchmarking
Modern HVAC systems often include energy recovery devices and environmentally friendly refrigerants. These
systems require proper modeling and performance verification. Cavique applied axiomatic design principles
and explained that adding energy recovery systems often requires complete system redesign rather than
simple modification.
[10]
Cortella et al. modeled integrated HVAC and refrigeration systems in supermarkets
using TRNSYS simulation software.
[21]
D’Agaro et al. studied transcritical CO₂ systems and reported that system
performance strongly depends on climate conditions and accurate thermodynamic modeling.
[22]
Zafirah and
Mardiana experimentally tested air-to-air energy recovery systems under high-humidity conditions and
reported that environmental factors significantly affect system performance.
[23]
Zhou et al. compared different
Building Energy Modeling (BEM) software programs and reported differences between predicted and actual
HVAC performance.
[7]
Recent structured HVAC delivery methodologies also emphasize integrated lifecycle
coordination and performance verification to minimize design–operation gaps, reinforcing the importance of
structured stage-gated frameworks.
[24]
Emmerich and Persily discussed the limitations of indoor air quality
(IAQ) simulation models and reported that modeling alone cannot guarantee real performance.
[25]
Sadr
Haghighi emphasized the importance of CFD simulation in predicting airflow and thermal behavior in
buildings.
[26]
Overall, these studies highlight that simulation and modeling must be supported by
commissioning and benchmarking procedures to verify actual HVAC system performance.
[7,21]
Based on the
literature gaps identified above, the proposed framework is developed as shown in Fig. 1.
Fig. 1: Detailed Stage-Related HVAC Delivery and Performance Assurance Framework.
The framework divides the HVAC project into four major phases: initiation and analysis, detailed engineering,
execution, and commissioning and handover. Each phase includes clear decision points (gates) and feedback
loops to ensure that errors are corrected before moving to the next stage. In this framework, a decision gate
acts as a formal review checkpoint where technical performance, design completeness, and compliance with
project requirements are evaluated before progressing to the next phase. If the defined performance criteria
are not satisfied, a feedback loop is triggered, requiring corrective modifications in the previous stage before
approval is granted.
3. Proposed HVAC framework
3.1 Framework overview
On the basis of the reviewed literature, a structured stage-gated HVAC framework is proposed. The framework
divides the HVAC project into four major phases; each phase includes clear decision points (gates) and
feedback loops to ensure that errors are corrected before moving to the next stage.
[4,8]
Research has shown
that many HVAC performance problems occur because there is no effective connection between design,
installation, and operation.
[10]
Therefore, this framework ensures alignment between technical design
methods, construction practices, and performance verification. The structure is influenced by energy
efficiency guidelines and systematic design principles recommended in previous studies.
[1,9]
The main
objective of this framework is to reduce the gap between predicted and actual energy performance, improve
Indoor Environmental Quality, and ensure the long-term reliability of HVAC systems.
[22,25]
As shown in Fig. 2,
the proposed framework consists of four major phases separated by decision gates. Each phase includes
verification checkpoints and feedback loops. If performance targets are not satisfied at any gate, the process
returns to the previous phase for corrective modification, ensuring systematic performance assurance.
Fig. 2: Simplified life cycle representation of the stage-gated HVAC delivery and performance assurance framework.
3.2 Phase 1: Initiation and analysis
The first phase focuses on defining project goals and collecting necessary building information. This includes
identifying owner requirements, occupancy schedules, indoor comfort targets, and energy performance
expectations.
[1,16]
Building-related data collection is essential at this stage. Information such as architectural
layout, orientation, insulation levels, glazing ratio, shading devices, and envelope materials must be
documented.
[18]
Climatic data such as outdoor temperature, humidity, solar radiation, and wind conditions are
also needed for accurate load estimation.
[5]
Heating and cooling load calculations are performed using
standard procedures from the ASHRAE Handbook.
[1]
Ventilation losses due to infiltration and air exchange
must also be included, as research shows that nearly 48% of heating energy can be lost through uncontrolled
air change processes.
[5]
Energy efficiency standards such as building performance requirements must be
considered during system planning.
[27]
An early evaluation of ventilation strategies and passive design options
is recommended on the basis of previous research findings.
[17]
After load calculations are completed and
alternatives are evaluated, a decision gate confirms the selection of the HVAC system type, such as split system,
variable refrigerant flow (VRF), or a chiller-based centralized system. If performance targets are not satisfied,
the design process returns to data review and modification following axiomatic design principles.
[10]
This
feedback loop ensures that incorrect assumptions are corrected before the detailed design begins.
3.3 Phase 2: Detailed engineering
In this phase, the selected HVAC system is converted into detailed engineering drawings and technical
specifications. Equipment capacities, space requirements, and system configurations are finalized on the basis
of the calculated loads and performance objectives.
[4,22]
Equipment layout planning ensures the proper
placement of chillers, air handling units (AHUs), cooling towers, pumps, and indoor units. Duct routing and
piping layouts are prepared while considering structural and architectural constraints.
[28]
Computational fluid
dynamics (CFD) analysis may be used to evaluate airflow distribution, temperature uniformity, and ventilation
effectiveness where needed.
[19,26]
Accurate airflow modeling improves comfort and reduces design errors.
[26]
The
duct design follows the SMACNA standards for pressure class, leakage control, and material thickness,
whereas thermal sizing, airflow distribution, and HVAC system design parameters may also be aligned with
CIBSE Guide B recommendations.
[28,29]
Control strategies and sequences of operations are prepared according
to intelligent automation principles and high-performance control guidelines.
[3,9]
Energy modeling software
may be used to predict system performance; however, studies have shown that simulation results can differ
from real operation, so assumptions must be carefully validated.
[7]
Interdisciplinary coordination meetings are
held to ensure compliance with mechanical, electrical, and architectural requirements.
[27]
A second decision
gate confirms that all the design documents are complete. The decision criteria include verification of
equipment sizing, duct and piping layout coordination, control strategy validation, and compliance with
engineering standards such as the SMACNA and CIBSE guidelines.
[28,29]
3.4 Phase 3: Execution
The execution phase includes the procurement, delivery, and installation of HVAC equipment and
materials.
[4,21]
Equipment must meet design specifications and manufacturer requirements.
[22]
Installation
activities include duct fabrication, piping installation, insulation work, equipment mounting, electrical
connections, and control wiring. Proper installation practices are necessary to prevent leakage, vibration, and
thermal losses.
[5,28]
Quality control inspections are conducted at different stages of installation. These
inspections verify alignment with design drawings and technical standards.
[6,25]
Any nonconformities or
installation defects must be corrected before system startup. Research indicates that many HVAC systems
underperform because of improper installation rather than design issues.
[7]
Therefore, this framework
emphasizes documentation, checklists, and inspection records during execution. A third decision gate ensures
that the installation quality is verified before commissioning.
3.5 Phase 4: Commissioning and handover
Commissioning is among the most critical stages of the framework because it provides a structured process
for verifying and documenting that HVAC systems meet defined performance requirements throughout project
delivery and operation. Precommissioning activities include equipment inspection, system flushing, pressure
testing, sensor calibration, and control verification.
[9,11]
Testing, adjusting, and balancing (TAB) procedures are
performed in accordance with standards developed by the National Environmental Balancing Bureau.
[6,23]
TAB
ensures correct airflow rates, water flow rates, and pressure balance in the system. Functional performance
testing evaluates system behavior under different operating conditions, including partial-load and peak-load
situations.
[25]
This step confirms whether the control sequences operate correctly and whether the indoor
environmental conditions meet the design targets. The performance data are collected for benchmarking and
comparison with the simulation results.
[13,22]
Studies have shown that differences often exist between modeled
and actual performance, so measured data are essential for validation.
[7]
Final documentation includes
operation and maintenance (O&M) manuals, as-built drawings, commissioning reports, and operator training
records.
[5,15]
Proper training ensures that building operators can maintain system efficiency throughout the
building lifecycle. A final decision gate confirms that the performance criteria are satisfied before official
handover. The performance validation includes TAB, functional performance testing, and verification of indoor
environmental conditions such as the airflow rate, temperature stability, and energy consumption
benchmarks.
[6,25]
Ongoing monitoring and periodic recommissioning may be recommended to maintain long-
term efficiency and reliability.
[9,25]
4. Discussion
4.1 Integration of research findings
The proposed framework integrates key findings from previous research. Studies have shown that intelligent
control systems can improve HVAC energy efficiency when supported by reliable data and commissioning
procedures.
[3,4,11]
Research on passive design highlights the influence of ventilation and infiltration on energy
use and indoor comfort.
[17,18]
In addition, several studies report differences between simulated and actual
HVAC performance, emphasizing the need for systematic verification and commissioning procedures.
[7]
4.2 Benefits of the stage-gated structure
The stage-gated approach ensures that each phase is completed and verified before moving to the next
stage.
[10]
Decision gates reduce design errors and prevent costly rework. Feedback loops allow corrections if
performance targets are not achieved.
[7]
Coordination between architects, engineers, and contractors improves
system reliability and reduces the degree of comfort problems reported in ventilation studies. Proper
documentation also supports long-term maintenance and efficiency.
[23,26]
The effectiveness of the proposed
framework can be assessed using measurable project and operational performance indicators. These include
the deviation between the predicted and measured energy consumption, commissioning duration,
installation-related rework, and lifecycle cost. A comparison of the predicted and measured energy
performance provides an objective measure of the building energy performance gap.
[12]
The stage-gated
structure is intended to identify design and execution deficiencies before progression to subsequent stages,
thereby providing opportunities to reduce commissioning delays and corrective rework. Lifecycle cost analysis
can further be used to assess the economic implications of design decisions and long-term system operation.
These indicators provide a practical basis for future quantitative evaluation of the proposed framework. These
indicators are proposed in Table 1 as evaluation measures rather than as experimentally measured outcomes
of the present study, and their quantitative assessment across multiple operational buildings represents an
important area for future validation.
The comparison in Table 2 highlights how the proposed framework is intended to improve reliability, reduce
rework, and strengthen lifecycle performance assurance compared with traditional execution models. Recent
developments in building commissioning and HVAC performance assurance have increasingly emphasized
integration across design, construction, commissioning, and operation. ASHRAE Guideline 1.1-2025 describes
the commissioning process for HVAC&R systems across project delivery phases from predesign through
occupancy and operation, including design and submittal reviews, verification against project requirements,
documentation of issues and resolutions, and training of operations and maintenance personnel.
[30]
Similarly,
recent HVAC research has explored BIM-based integration with building management system data and
automated fault detection and diagnostics, demonstrating the potential for maintaining digital information
continuity between HVAC systems and building operations.
[31]
The present framework focuses primarily on the
project-delivery structure itself by organizing HVAC activities into defined phases, decision gates, and
corrective feedback loops. The proposed framework therefore complements technology-oriented approaches
by providing a structured delivery and verification sequence into which BIM, digital monitoring, and advanced
commissioning tools can subsequently be integrated.
Table 1: Suggested Performance Indicators for the Evaluation of the Proposed Framework
Performance indicator
Measurement basis
Energy-performance deviation
Difference between predicted and measured HVAC energy consumption
Commissioning duration
Time from commencement of commissioning to performance acceptance
Installation rework
Number or percentage of corrective installation activities
Lifecycle cost
Initial, energy, maintenance, and replacement costs over the assessment period
Table 2: Comparison between Traditional HVAC Execution and the Proposed Framework
Parameter
Traditional HVAC Approach
Proposed Stage-Gated Framework
Project Phases
Linear and loosely connected
Structured and phase-verified
Design–Installation Link
Weak coordination
Integrated through decision gates
Commissioning
Often last-stage formality
Structured and performance-driven
Energy Benchmarking
Rarely validated
Mandatory verification at each stage
Feedback Mechanism
Minimal
Built-in corrective loops
Performance Gap Risk
High
Reduced through validation gates
Documentation
Basic handover documents
Lifecycle documentation & benchmarking
4.3 Applicability and limitations
The framework can be applied to residential and commercial buildings.
[1,16]
However, it requires trained
commissioning professionals and sufficient project resources.
[6]
Local building codes and energy standards
must also be considered during implementation.
[27,32]
Overall, the framework provides a structured method to
improve HVAC performance and reduce energy gaps between design and operation.
[22]
5. Limitations and future work
Although the proposed framework provides a structured methodology for HVAC performance assurance,
several limitations should be acknowledged. First, the present research is primarily based on simulation
results and a limited set of historical building data. Therefore, the proposed approach may not fully capture
real-time operational variations in buildings or extreme environmental conditions that can occur in practical
scenarios.
[9]
Second, the current model assumes relatively stable operating conditions and does not explicitly
consider factors that can significantly influence HVAC system performance, such as occupant behavior,
equipment degradation, and unexpected operational disturbances.
[21]
Future research should address several
areas to further enhance the proposed framework:
Real-world implementation: The proposed framework should be applied and validated in operational
buildings to evaluate its effectiveness under practical conditions and across different climatic regions.
[13]
Improved predictive models: The framework can be further enhanced by incorporating advanced data-
driven and machine learning techniques to improve prediction accuracy and system adaptability.
[11]
Adaptive control integration: Future work should explore the integration of real-time monitoring and
intelligent control strategies to improve HVAC system performance, energy efficiency, and operational
reliability.
[27]
Future development may also integrate building information modeling (BIM) and digital twin
technologies to improve information continuity and lifecycle performance tracking. IoT-based continuous
commissioning and AI-assisted predictive maintenance could further support real-time performance
monitoring and early identification of equipment degradation.
[33]
6. Case Study: application of the proposed framework
To illustrate the practical applicability of the proposed framework, the methodology is mapped to a recently
published HVAC system optimization study for a six-story commercial building.
[24]
In reference to this study,
detailed heat load estimation and economic evaluation were conducted for a mid-rise commercial building
located in a warm climatic region. The total cooling load was estimated at approximately 110–115 TR, and a
VRF system was selected on the basis of lifecycle cost analysis and zoning flexibility.
[24]
To provide a practical
illustration of the framework, the selected VRF system capacity closely matched the estimated cooling load,
demonstrating appropriate system sizing and design verification. Previous research has indicated that
inaccurate load estimation and insufficient verification can contribute to HVAC equipment oversizing.
[7]
By
introducing structured decision gates during the load calculation and system selection stages, the proposed
framework helps ensure that the system capacity remains aligned with the calculated thermal loads. This
structured approach is intended to reduce oversizing risk and support improved energy performance and
lifecycle efficiency. The analytical mapping of the case study to the proposed stage-gated framework is
summarized in Table 3.
[24]
The methodology adopted inherently follows several elements of the proposed
stage-gated framework.
[24]
However, the present framework formalizes these checkpoints into defined
decision gates and feedback loops, thereby providing a structured approach to lifecycle performance
assurance beyond conventional design-economic analysis.
Phase 1– Initiation and Analysis: In the referenced study, climatic data, occupancy schedules, and building
envelope characteristics were collected to compute peak cooling loads using standard procedures.
[24]
Ventilation and infiltration losses were incorporated into the total sensible and latent heat estimation. This
aligns with Phase 1 of the proposed framework, where structured data collection and load verification
precede system selection.
Phase 2–Detailed Engineering: The case study applied systematic equipment sizing and performance
evaluation. Alternative HVAC systems (VRF, chilled water, and DX split systems) were compared before the
final selection.
[24]
This corresponds to Decision Gate 2 in the framework, ensuring that system selection is
validated against performance and economic criteria.
Phase 3– Execution: The referenced work included cost estimation and installation considerations, ensuring
alignment between the calculated load and system capacity.
[24]
Proper installation planning reduces the risk of
performance deviation.
Phase 4–Commissioning and Performance Benchmarking: Although the referenced study focused
primarily on design and economic evaluation, its structured load calculation and performance justification
approach
reflect the commissioning-readiness philosophy embedded in the proposed framework. By mapping the
design and economic analysis presented in the proposed stage-gated framework, the analysis indicates that
structured decision checkpoints can improve transparency, reduce oversizing risk, and support lifecycle
energy optimization.
[24]
The principal activities, decision criteria, stakeholders, and deliverables, including design reports [10],
installed systems, commissioning reports, and performance reports, are summarized in Table 4.
[4,7,11,17,18]
Table 3: Analytical Mapping of the Case Study to the Proposed Stage-Gated Framework
[24]
Framework Phase
Observed Outcome
Framework Value
Phase 1 – Initiation &
Analysis
Cooling load ≈ 110–
115 TR
Prevents oversizing and
ensures accurate baseline
Decision Gate 1
VRF shortlisted
Structured elimination of
unsuitable systems
Phase 2 – Detailed
Engineering
Optimized lifecycle
cost range
Integrates economic
validation early
Decision Gate 2
VRF selected
Reduces long-term
operational inefficiency
Phase 3 – Execution
Planning
Cost range ₹5.7–6.9
million
Budget alignment before
deployment
Phase 4 –
Commissioning
Readiness
Performance aligned
with predicted load
Minimizes design–operation
performance gap
Table 4: Summary of the Proposed HVAC Performance Framework
Phase
Key Activities
Decision Criteria
Stakeholders
Deliverables
Design
Load calculation, HVAC planning
Meets energy and
comfort targets
Design engineers
Design reports
[10]
Installation
Equipment, duct and piping
installation
Installation follows
specifications
Contractors
Installed system
[7]
Commissioning
TAB, system verification
System meets design
performance
Commissioning
engineers
Commissioning report
[4,11]
Operation
BAS, maintenance
Energy and comfort
maintained
Facility managers
Performance reports
[17,18]
7. Conclusion
This paper presents a structured stage-gated HVAC delivery and performance assurance framework aimed at
reducing the gap between predicted and actual building performance. The framework integrates technical
design, interdisciplinary coordination, systematic execution, and performance validation into a unified
lifecycle approach. Unlike conventional HVAC project execution models, which often treat design, installation,
and commissioning as separate tasks, the proposed structure introduces defined decision gates and corrective
feedback loops. This is intended to promote accountability, reduce oversizing risk, improve coordination, and
support lifecycle performance reliability. The analytical mapping of a published HVAC optimization study
demonstrates that many high-performing projects inherently follow the elements of a structured approach.
However, formalizing these steps into a clearly defined framework strengthens transparency and repeatability.
Future work may involve applying this framework to multiple real-world projects to quantitatively evaluate
measurable indicators such as energy-performance deviation, commissioning duration, installation rework,
and lifecycle cost. These indicators can provide an objective basis for assessing the effectiveness of the
proposed framework across operational buildings. Overall, the proposed model provides a practical and
adaptable roadmap for improving HVAC system delivery in residential and commercial buildings. Future
development may further integrate BIM, digital twin technologies, IoT-based continuous commissioning, and
AI-assisted predictive maintenance to support continuous performance assurance.
Acknowledgement
None
CRediT Author Contribution Statement
Amey S. Wagh: Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Visualization,
Writing – Original draft. Naresh Anabathula: Investigation, Validation, Writing – Review & editing. Saud
Sakharkar: Investigation, Validation, Writing – Review & editing. Anas Shaikh Sharif: Investigation,
Validation, Writing – Review &editing. Mohammed Anas Bijapure: Investigation, Validation, Writing –
Review &editing. All authors have read and approved the final version of the manuscript for publication and
agree to be accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity
of any part of the work are appropriately investigated and resolved.
Funding Declaration
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-
profit sectors.
Data Availability Statement
All data analyzed during this study are cited in this published article.
Conflict of Interest
There is no conflict of interest.
Artificial Intelligence (AI) Use Disclosure
The authors declare that artificial intelligence (AI)-assisted tools were used only for language refinement,
grammar improvement, and manuscript structuring purposes during the preparation of this work. All
technical content, analysis, and interpretations were independently developed and verified by the authors.
Supporting Information
Not applicable.
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