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Article

HVAC System Energy Retrofit for a University Lecture Room Considering Private and Public Interests

by
Diana D’Agostino
,
Federico Minelli
and
Francesco Minichiello
*
Department of Industrial Engineering, University of Naples Federico II, P.le Vincenzo Tecchio 80, 80125 Naples, Italy
*
Author to whom correspondence should be addressed.
Energies 2025, 18(6), 1526; https://doi.org/10.3390/en18061526
Submission received: 12 February 2025 / Revised: 14 March 2025 / Accepted: 17 March 2025 / Published: 19 March 2025
(This article belongs to the Section G: Energy and Buildings)
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Abstract

:
The operation of Heating Ventilation and Air Conditioning (HVAC) systems in densely occupied spaces results in considerable energy consumption. In the post-pandemic context, stricter indoor air quality standards and higher ventilation rates further increase energy demand. In this paper, the energy retrofit of a partial recirculation all-air HVAC system serving a university lecture room located in Southern Italy is analyzed. Multi-Objective Optimization (MOO) and Multi-Criteria Decision-Making (MCDM) approaches are used to find optimal design alternatives and rank these considering two different decision-makers, i.e., public and private stakeholders. Among the Pareto solutions obtained from optimization, the optimal alternative is identified, encompassing three Key Performance Indicators and using a new robust MCDM approach based on four methods, i.e., TOPSIS, VIKOR, WASPAS, and MULTIMOORA. The results show that, in the post-pandemic era, baseline retrofit scenarios for infection reduction that do not involve the introduction of demand control ventilation strategies cause energy consumption to increase from negligible values up to 59%. On the contrary, baseline retrofit scenarios involving demand control ventilation strategies cause energy consumption to decrease between 5% and 38%. The findings offer valuable guidance for HVAC system retrofits in higher education and similar buildings, emphasizing the potential to balance occupant health, energy efficiency, and cost reduction. The results also highlight significant CO2 reductions and minimal impacts on thermal comfort, showcasing the potential for substantial energy savings through targeted retrofits.

1. Introduction

Recent research in the energy sector focuses on strategies toward higher efficiency [1,2,3,4] and new pathways toward low-energy buildings are investigated in diverse geographical and economic areas [5,6,7,8,9]. Although the exploitation of renewable energy sources is gaining a significant role in offsetting the energy demand of cities [10,11,12,13], building systems such as Heating, Ventilation, and Air-Conditioning (HVAC) still consume a lot of energy [14]. The COVID-19 pandemic has intensified this challenge by increasing the need for enhanced air renewal to ensure safe Indoor Air Quality (IAQ) and lower virus transmission risks. [15,16]. Adapting existing HVAC systems in the post-pandemic era can enhance IAQ and lower airborne virus transmission risks while improving system performances [17,18].
However, stricter ventilation requirements have raised energy expenses for operating HVAC systems in existing buildings [19]. This calls for new retrofit paradigms able also to mitigate this underrated pandemic impact on the energy sector [20].
This study contributes to the international debate on SARS-CoV-2’s airborne transmission [21]. This mechanism of transmission is one of the most important in the study of HVAC systems, which can be effectively used to reduce it [19]. Recent studies have examined aerosol hazards and the potential of air renewal to mitigate them in several building typologies [22,23,24,25,26].
When designing an HVAC system retrofit, it is important to balance energy, environmental, comfort, and economic performance [27,28,29]. These and other related studies highlight the need for innovative HVAC retrofit strategies that can reconcile energy, comfort, and health needs [30,31,32]. Given the many variables in HVAC retrofit planning, often no single solution excels in all performance objectives, so optimal choices must be identified among several sub-optimal options [33]. Assuming the multi-faceted nature of building energy challenges, multi-objective optimization (MOO) techniques have become indispensable tools for reconciling competing goals [34,35]
The MOO technique is a powerful optimization strategy widely spread in the energy field [36,37] and is often coupled with dynamic energy simulation (a technique used to obtain the energy consumption of buildings [9,38]). It allows us to easily find optimal solutions under multiple objectives or criteria, measured by relevant Key Performance Indicators (KPIs). Genetic algorithms (GAs) are commonly employed as the engine of the MOO process to optimize two or more objectives simultaneously [39]. MOO frameworks have been applied to renewable energy system design [37,40], energy storage systems [41,42], and the integration of distributed generation resources [43,44,45], underscoring the generality of MOO methods. Also, some applications of this technique on HVAC systems are found for the optimization of cold thermal storage [46,47], HVAC system control [48], and system operation management [49].
In particular, Afsharpanah et al. [46,47] investigated the charging performance of a cold thermal energy storage container equipped with two rows of serpentine tubes and extended surfaces. Their numerical study showed that by optimizing key parameters, the charging rate can be increased by 18%, thereby enhancing the efficiency of cold thermal energy storage and leading to notable improvements in charging efficiency. This study underscores the potential of multi-objective optimization to fine-tune HVAC system components, achieving a delicate balance between relevant performance objectives, and further illustrates the broad applicability of MOO frameworks across various energy-related disciplines.
The increasing application of similar methods highlights the multifaceted nature of building energy challenges and the critical need for new multi-objective approaches to develop effective energy retrofit strategies. Furthermore, a comprehensive approach that also considers the selection of Pareto optimal designs according to stakeholder preferences has never been used.
In this study, the MOO approach allows us to find optimal alternatives in the large design space defined by the several retrofit design variables available and a Multi-Criteria Decision-Making (MCDM) analysis allows us to select the best Pareto solutions for different stakeholders. Contrasting stakeholder interests in retrofit decisions, indeed, underscore the need for an approach that accounts for their varying priorities in energy, comfort, and health safety. MCDM methods can rank the best alternatives from the optimization process, helping identify compromise solutions that take into account contrasting stakeholder interests.
The MCDM approach comprehends several methods to address multi-objective problems [50,51]. The use of different MCDM objectives or criteria can be considered to make decisions, and it is a straightforward means to prioritize technology alternatives [52]. Furthermore, the MCDM approach facilitates the management of different stakeholders by enabling the incorporation of diverse preferences and priorities into the decision-making process [53]. By allowing the integration of weights for the various criteria that reflect the interests of multiple parties, MCDM ensures that the perspectives of all relevant stakeholders are considered. This inclusive approach enhances the transparency and fairness of the decision process while aligning the objectives of different stakeholders. It ensures that the financial and operational goals of building owners are harmonized with the broader societal and environmental objectives set by policymakers [53].
Although some applications of these techniques exist, previous studies have rarely integrated these methods in a robust manner to capture the full complexity of HVAC system retrofit challenges.
In summary, in post-pandemic conditions, a comprehensive evaluation of optimal improvements in existing HVAC systems is needed to reduce the risks linked to airborne disease transmission among occupants, while limiting energy consumption and related emissions. This paper aims to identify optimal retrofit interventions for a case study university lecture room, located in Naples, Southern Italy. The methodology of this work couples dynamic energy simulation and the MOO and MCDM approach when different retrofit choices of an all-air HVAC system are available. The case study lecture room is analyzed under different scenarios of the HVAC system operation (with and without partial air recirculation) and configuration (with and without the installation of an inverter and automatic dampers). The definition of the robustness of MCDM given by Brauers and Ginevicius in [54] is considered to provide reliable results, avoiding that method-specific bias may influence the results. For this purpose, the research employs four different MCDM methods, i.e., the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) [55,56], VlseKriteriujumska Optimizacija I Kompromisno Resenje (VIKOR) [57], Weighted Aggregated Sum Product Assessment (WASPAS) [58], and MULTIMOORA [59], i.e., the Full Multiplicative Form of Multi-Objective Optimization by Ratio Analysis (MOORA). Then, the ensemble ranking approach used in [60] is applied to calculate a final ranking that averages the outcomes of all the considered MCDM approaches. Multiple stakeholder preferences are assessed, i.e., public and private stakeholders. The Analytic Hierarchy Process (AHP) [61] is applied to calculate specific weights that reflect the importance given to each criterion by the different stakeholders.

Aims, Objectives, and Innovative Aspects

The main aim of this paper is to identify and rank optimal retrofit interventions for a university lecture room’s all-air HVAC system in Naples, Southern Italy, taking into account the twofold requirement of maintaining safe indoor air quality and ensuring efficient energy performance. A new framework that allows the consideration of different stakeholders’ interests is proposed. Specifically, the study seeks to obtain the objectives below reported.
  • Assess the impact of different operation and configuration scenarios for the existing HVAC system (covering partial or full outdoor air supply, with or without the use of demand control ventilation) on the energy consumption of the system;
  • Apply a comprehensive methodological framework that couples dynamic energy simulation, multi-objective optimization (MOO), and multi-criteria decision-making (MCDM) techniques in order to explore a wide range of retrofit solutions addressing several Key Performance Indicators representing energy consumption, environmental impact, thermal comfort, and economic viability;
  • Evaluate multiple stakeholder perspectives by capturing varying priorities (i.e., public vs. private interest);
  • Use multiple MCDM methods and the ensemble ranking approach proposed by Mohammadi and Rezaei to provide a robust ranking of potential retrofit alternatives;
  • Recommend best compromise retrofit solutions that effectively reduce airborne infection risk while balancing energy and economic considerations.
By addressing infection control, energy use, environmental impact, occupant thermal comfort, indoor air quality, and cost-effectiveness, the study offers guidelines for HVAC system retrofits in higher education and similar buildings, balancing health and operational costs. By merging MOO with robust MCDM, the proposed approach not only improves the accuracy of energy consumption predictions and retrofit performance evaluations but also ensures that the decision-making process is transparent and balanced. This integrated approach effectively aligns the operational and financial goals of building owners with broader societal and environmental objectives, thus advancing the state-of-the-art HVAC system retrofit strategies. To the authors’ knowledge, this global retrofit approach is an innovative contribution to the literature.

2. Methodology

The framework used in this paper is presented here in synthesis (Figure 1). The first step is the creation of a calibrated energy model using on-site measurements and investigations and the as-built documents of the university lecture room. Four design scenarios (later referred to as A0, A, B0, and B and better described in the next section) are defined to represent baseline energy retrofit alternatives of the HVAC system. These alternatives are evaluated from an energy efficiency, thermal comfort, and economic point of view.
For the best-performing scenarios, a deeper energy retrofit intervention (i.e., replacement of heat generators, etc., better described in the next section) is evaluated by MOO and MCDM approaches, considering the performance of several deep retrofit alternatives (given by the combination of multiple design variables) under the following KPIs (to be minimized):
  • Annual CO2-eq emissions [kgCO2-eq];
  • Annual thermal discomfort hours [h];
  • Investment cost [EUR].
The selection of these three KPIs is driven by the importance given to these criteria by the decision-makers involved in the study. In fact, CO2 reduction is an important aspect of retrofit actions from the standpoint of policymakers and it is closely linked to energy savings. The number of discomfort hours in a university classroom plays a key role in the thermal and hygrometric well-being of individuals. Finally, for a private stakeholder, the investment cost is considered more significant than other ones, such as maintenance costs.
Annual dynamic energy simulation is performed for all analyzed alternatives upon the calibrated energy model by means of DesignBuilder software 6.2.3 [62] that uses the engine EnergyPlus [63]. The CO2-eq emissions are evaluated considering only the operational emissions related to energy savings due to the retrofit intervention. To this scope, a CO2 emission factor equal to 0.46 kgCO2-eq/kWhels is set for electric energy. This value is based on ISPRA data for 2024 and due to a lack of knowledge of the specific energy mix of the building energy supplier, this factor considers that the totality of the electric energy is produced by fossil fuel. Discomfort hours are evaluated on the outcomes of the dynamic energy simulation. Costs of retrofit interventions are calculated considering the cost of each component (for supply and installation), using local price lists.
Multi-objective optimization (MOO) is used to minimize two main factors: CO2 emissions and hours of thermal discomfort. These two goals often conflict-improving one can make the other worse. To address this, the optimization process looks at a wide range of design variables related to the lecture room, such as building envelope changes (like insulation or window upgrades) and HVAC system modifications.
Genetic algorithms (GAs) are used to solve this problem. These algorithms are inspired by natural selection and start with a group of possible solutions, each represented by a set of design variables. The algorithm then improves the solutions through selection, combining parts of different solutions (crossover) and random changes (mutation). Over time, the algorithm finds solutions that offer the best balance between CO2 emissions and thermal discomfort.
The result is a set of optimal solutions called the Pareto Front. Each solution on the Pareto Front represents a different trade-off: improving one goal would worsen the other. This approach gives decision-makers several strong options, allowing them to choose the best solution based on their priorities, balancing environmental impact and occupant comfort.
Among the Pareto solutions obtained from optimization, the optimal alternative is identified, by MCDM, encompassing all three KPIs, using an innovative robust approach proposed in [53], based on four multi-criteria methods (TOPSIS, VIKOR, WASPAS, MULTIMOORA). These methods are chosen for the different approaches used in ranking the design alternatives to provide diversity in the results of individual MCDM method applications and increase the approach’s robustness.
TOPSIS is an MCDM approach [56] that ranks alternative solutions by calculating the distances of all the available alternatives from two ideal alternatives (positive and negative). Also, the VIKOR method is based upon the calculation of distances between alternative solutions, but this approach chooses the best solution only considering closeness to an ideal positive solution [64]. WASPAS stems from the integration of two well-known MCDM methods: the Weighted Sum Model (WSM), which ranks alternative solutions by applying an additive utility function, and the Weighted Product Model (WPM), which uses a multiplicative utility function [58]. MOORA has been developed by Brauers and Zavadskas in 2006 to find solutions to MCDM problems. The Full Multiplicative Form of MOORA (MULTIMOORA) is a further development of the MOORA method and was developed in 2010 by the same authors [65].
MCDM is performed for two decision-makers with conflicting interests, i.e., a public stakeholder corresponding to the policymakers and a private stakeholder (building owner). The weight of the MCDM criteria (corresponding to the KPIs) is evaluated considering that the public stakeholder (policymakers) gives more relevance to the savings in terms of CO2-eq emissions, while the private one gives more importance to the cost of the intervention. The evaluation of weights assigned to the considered criteria stems from the investigation of preferences expressed by involved decision-makers (DMs) and it is performed by Analytic Hierarchy Process (AHP) using the Saaty scale of judgment (Table 1).
In the AHP method, decision makers (DMs) perform pairwise comparisons of criteria based on their relative importance using Saaty’s scale, for instance, assigning a value of 3 for moderate importance and 6 for a rating between strong and very strong importance. Each element in the pairwise comparison matrix (as shown in Figure 2) is normalized by dividing it by the sum of its corresponding column. The average of the normalized values in each row then provides the normalized weight (Wi, norm) for that criterion.
For example, DM1 judged annual CO2-eq emission reduction as moderately (3) more important than reducing discomfort hours and between strong and very strong (6) more important than reducing investment cost, while discomfort hours were considered slightly more important than investment cost (value of 2). This process resulted in weights of 0.67, 0.22, and 0.11 for annual CO2-eq emission reduction, reduction in discomfort hours, and reduction in investment cost, respectively. For DM2, the process yielded weights of 0.09, 0.18, and 0.73 for the same criteria.
To ensure that the judgments are logically consistent, the Consistency Ratio (CR) is computed by comparing the Consistency Index (CI) of the matrix to a Random Consistency Index (RI) for matrices of the same size. A CR value below 0.1 is generally acceptable; in this case, a CR of 0 for both DMs indicates perfect consistency in their judgments. Here, DM1 reflects the collective or policymakers’ interests, while DM2 represents the priorities of private building owners, ensuring that both perspectives are reliably integrated into the retrofit intervention design.
AHP method’s equations and calculation procedure are not reported in this paper for brevity but can be found in [53].
TOPSIS, VIKOR, WASPAS, and MULTIMOORA are applied, considering the different weights allocated by the two DMs to obtain four rankings of the considered alternatives. Since different methods lead to different ranking results, an ensemble ranking is calculated, with the approach used by Mohammadi and Rezaei in [60], allowing the derivation of two final rankings (one for each DM). The new approach is based on the half-quadratic (HQ) theory. The approach determines an optimal weight for each one of the considered MCDM methods. The weights are then used to compute the aggregated final ranking.
A comparative analysis of the final rankings is lastly performed between ensemble rankings obtained for the two different analyzed DMs. Both the positive and negative discrepancies between the rankings of retrofit alternatives are analyzed to emphasize how the two decision makers’ conflicting preferences influence the outcomes.

3. Case Study and Proposed Energy Retrofit Solutions

A case study energy retrofit of an all-air HVAC system serving a university lecture room located in Naples (South Italy) is analyzed. The mentioned system is composed of an Air Handling Unit (AHU), air ducts, diffusers, and vents serving the lecture room. Considering the existing configuration of the HVAC system, used as baseline, the AHU works with 60% of recirculation air (40% of outside air). In Figure 3, the HVAC system layout is reported. The existing heat generators are an obsolete methane gas boiler (efficiency equal to 0.91) and an obsolete air-cooled chiller (energy efficiency ratio EER equal to 3.0) that provide heated and refrigerated water to the AHU.
A dynamic energy analysis of this system is carried out using the DesignBuilder 6.2.3 software. The calibration of the model for a representative summer season day is reported in Figure 4. As can be seen from the measured temperature, there are two peaks. These are due to a decrease in the endogenous thermal load caused by people leaving the classroom during that time slot. Students begin to leave the room at 13:00 and by 14:0 there are no students in the classroom. After 14:00 PM, students gradually begin to return to the classroom. The calibrated model is used to compare four different retrofit design strategies.
The proposed retrofit scenarios consider two fundamental aspects, i.e., the adaptation to the new COVID-19 protocols and the limitation of energy expenditures and related CO2-eq emissions. The first aspect is guaranteed by the use of high-efficiency filters or an increase in external airflow. The second aspect is guaranteed by the installation of an inverter device on the fan, automatic dampers, and indoor CO2 sensors, allowing a demand control ventilation protocol. All the scenarios are compared to the baseline, i.e., the configuration of the existing HVAC system.
The first two retrofit scenarios (A0 and A) involve keeping the system in partial recirculation mode, as follows:
  • Scenario A0: operation of the system with 60% outdoor air + HEPA (High-Efficiency Particulate Air) filter in the recirculation duct;
  • Scenario A: operation of the system with 60% outdoor air + HEPA filter + installation of inverter devices on the fans for airflow modulation + replacement of existing manual control dampers with automatic dampers.
The second two retrofit scenarios (B0 and B) involve the conversion of the system to a 100% external air one with no recirculation, as follows:
  • Scenario B0: operation of the system with 100% outdoor air;
  • Scenario B: operation of the system with 100% outdoor air + installation of inverter devices on the fans for airflow modulation + replacement of manual control dampers with automatic ones.
To simulate these scenarios, a detailed dynamic energy simulation model of the lecture room is developed. This model incorporates the specific modifications for each scenario and allows for the analysis of energy flows, indoor environmental quality, and HVAC system performance under the new COVID-19 protocols.
The decision to maintain partial recirculation in Scenario A0 and A is made to balance energy efficiency with infection risk reduction, as it permits a controlled mix of outdoor air and recirculated air, enhanced by high-efficiency filtration on the recirculation duct. For scenarios B0 and B, the simulation assumes a complete elimination of air recirculation to maximize the introduction of fresh outdoor air, a strategy aimed at further reducing airborne infection risks. This approach, however, typically leads to higher energy loads due to the continuous need to handle the full volumetric flow of outdoor air. The decision to simulate a 100% outdoor air system was driven by the requirement to evaluate the trade-off between superior indoor air quality and increased energy consumption.
Scenarios A0 and B0 are expected to provide lower energy efficiency than scenarios A and B but are considered the two least invasive means to reduce COVID-19 infection risk.
A deeper energy retrofit intervention is lastly analyzed only for scenarios A and B, using the proposed MOO and MCDM approach. Deep energy retrofit solutions entail further efficiency interventions, such as the replacement of the artificial lighting system, the replacement of the skylight and the solar shading, the replacement of the heat generators, and the variation in the indoor air temperature setpoint for both heating and cooling modes.
The evaluation of retrofit decisions A and B involves integrating the outputs from the dynamic energy model into a multi-objective optimization (MOO) framework, which generates a Pareto Front of optimal solutions based on competing objectives (i.e., minimizing CO2 emissions and thermal discomfort). These Pareto optimal solutions are further evaluated using a robust Multi-Criteria Decision-Making (MCDM) approach that incorporates stakeholder-specific preferences to rank the retrofit alternatives. This comprehensive optimization process is essential to capture the complex interactions between HVAC performance, energy consumption, and indoor environmental quality under the revised COVID-19 protocols.
MOO and MCDM are leveraged in this stage to identify the optimal solution for a private and public decision-maker, as reported in the methodology section. An analysis of thermal comfort is also provided by considering the PMV (Predicted Mean Vote) for scenarios A and B. Weights given for the two DMs to CO2-eq emissions, discomfort hours, and investment cost are calculated as 0.7, 0.22, and 0.11 and 0.09, 0.18, and 0.73 for DM1 and DM2, respectively.

4. Results and Discussion

Scenario A0 provides an increase of 29% in annual gas and 11% in electric energy consumption, compared to the existing system, due to the increase in outdoor air percentage (from 40% to 60%) and the installation of the HEPA filter. This increase is attributed to the additional energy required to handle a larger volumetric flow of outdoor air (which often highly differs in temperature from the indoor set point) and to the increased airflow resistance introduced by the HEPA filter, which forces the fan of the air handling unit to work harder. Contrariwise, scenario A provides a reduction of 38% in annual gas and 6% in electric energy consumption compared to the existing system. This reduction suggests that the demand control ventilation protocol used in scenario A effectively mitigates the thermal load despite the increased ventilation. For this reason, as expected, scenario A appears the least energy-intensive (see Figure 5 for reference).
Scenario B0 provides a 59% increase in annual gas consumption compared to the existing system while keeping electric energy consumption almost unchanged. This substantial increase in gas consumption likely results from the higher heating demand imposed by a greater intake of outdoor air, which the system struggles to handle efficiently without retrofit measures. Scenario B provides a reduction of 30% in annual gas and 5% in electric energy consumption compared to the existing system. Scenario B results in the least energy intensive compared to scenario B0 but does not outperform scenario A (Figure 5). The technical trade-offs in scenario B (between improved air quality from 100% outdoor air and the increased thermal load) explain its relatively lower performance. A simple PayBack period equal to 18 and 22 years is calculated for scenario A and scenario B, respectively.
Table 2 and Table 3 report detailed information on Pareto solutions and PMV values for scenarios A and B, respectively. Table 4 and Table 5 provide the results of the MCDM analysis for scenarios A and B, respectively, for both collectivity (DM1) and private (DM2) decision-makers.
Considering scenario A, for the public decision-maker (DM1), the Pareto solution corresponding to the optimal alternative (iteration 34, ranked as the first classified in Table 3 for DM1) provides a 39% reduction in CO2 emissions while maintaining unaltered the hours of discomfort; for the private decision-maker, another Pareto optimal solution (iteration 3) leads to 43% reduction in CO2 emissions with a 3% increase in hours of discomfort. These differences underscore that even small variations in design variables, such as cooling setpoints or glazing types, can significantly impact the trade-off between energy efficiency and thermal comfort.
In the case of scenario B, for both decision-makers, the optimal Pareto solution corresponds to the same alternative (iteration 6, ranked as the first classified in Table 4 for DM1), which provides a 42% reduction in CO2 emissions and an 11% increase in discomfort hours. This result suggests that the aggressive strategy of employing 100% outdoor air in scenario B imposes a higher thermal load that leads to increased discomfort. For this reason, it emerges that scenario A is preferable for both decision-makers because a significant reduction (39–43%) in CO2 emissions is obtained with a negligible variation in the hours of discomfort. However, scenario B leads to a reduction in CO2 emissions comparable to scenario A but a greater increase in hours of discomfort. The physical implication is that scenario A’s interventions better balance the thermal load and ventilation benefits, whereas scenario B’s approach, despite its air quality advantages, results in a less favorable comfort-energy trade-off. The results of the thermal comfort analysis show that the PMV is slightly better in scenario B, with reference to the solutions chosen by DMs (iteration 6 of scenario B vs. iterations 3 and 34 of scenario A). This may imply better local comfort conditions achieved in the case of a 100% supply of outdoor air, although it increases discomfort hours overall. However, it should be also noted that indoor air quality is better for scenario B compared to scenario A because only scenario B is characterized by 100% outdoor air.
The application of the MCDM leads to optimal solutions for both decision-makers. From a technical standpoint, the robust MCDM framework integrates diverse stakeholder preferences by weighing environmental, comfort, and cost KPIs, thereby ensuring that the selected solutions consider different parties’ needs. As said before, these solutions entail deep energy efficiency interventions, such as the replacement of the artificial lighting system, the replacement of the skylight and its solar shading, and the replacement of heat generators. These interventions not only enhance energy efficiency but also improve the thermal behavior of the building, reducing the overall heating and cooling loads. The results of the deep retrofit analysis are reported in Figure 6. Pareto fronts for CO2-eq emissions and discomfort hours; KPIs are retrieved from the MOO routine for both scenarios A and B. The investment cost of Pareto solutions is then evaluated to be fed into MCDM analysis. This integrated approach, combining MOO and robust MCDM, provides a comprehensive understanding of the interplay between technical, physical, and economic factors, thereby guiding the selection of retrofit solutions that optimally balance energy performance with occupant comfort and cost.
Divergences evidenced in preferred solutions by the two DMs can lead to suboptimal investment in energy efficiency measures if private stakeholders, primarily concerned with cost-effectiveness, resist upgrades that require higher upfront expenditures (even if these measures provide significant long-term environmental and comfort benefits). Conversely, public stakeholders may push for stricter standards that increase costs and potentially deter private investment, leading to a stalemate in retrofit progress.
To address these gaps, policymakers might consider creating incentive schemes (such as tax credits, subsidies, or low-interest financing) to lower the financial barrier for private stakeholders. Additionally, establishing stricter regulatory frameworks that set minimum performance standards can help align private actions with environmental objectives to attain long-term zero-emission targets [67]. Developing collaborative platforms where both stakeholder groups can negotiate and co-create retrofit strategies may also foster more balanced solutions. Moreover, incorporating advanced decision-support tools, like the MOO and robust MCDM methods described in this study, into policy planning can quantitatively illustrate the trade-offs and benefits, thereby facilitating more informed and mutually acceptable decisions.

5. Conclusions

This paper analyses the performance of an HVAC system for a university lecture room in Southern Italy from an energy, comfort, and environmental standpoint; firstly, baseline retrofit interventions were analyzed to consider two fundamental aspects, i.e., the adaptation to the new COVID-19 protocol and the impact on energy consumption.
Furthermore, an innovative approach based on Multi-Objective Optimization (MOO) and Multi-Criteria Decision-Making (MCDM) is applied to identify the optimal solution among other deep retrofit solutions proposed on the two best-performing baseline retrofit scenarios, considering both a public and a private decision-maker with conflictual interests, taking into account the CO2-eq emissions, thermal comfort, and financial aspects of the retrofit alternatives.
The existing configuration of the all-air HVAC system works with 60% of recirculation air (40% of outside air).
The main conclusions of the present research can be expressed as follows.
  • To reduce the risk of COVID infection, baseline retrofit scenarios (A0, i.e., operation of the system with 60% outdoor air + HEPA filter in the recirculation duct; B0, i.e., operation of the system with 100% outdoor air) cause energy consumption increases from negligible values up to 59% compared to the existing HVAC system outline;
  • Baseline retrofit scenarios for infection reduction also involve the installation of inverters and automatic dampers for demand control ventilation (A and B), causing energy savings between 5% and 38%;
  • In the case of scenario A (operation of the HVAC system with 60% outdoor air + HEPA filter + inverter and automatic dampers), for the implementation of deep retrofit intervention solutions (the replacement of the artificial lighting system, the skylight with its solar shading and the heat generators) the following can be said:
    ○
    There is no agreement between the two decision makers (private and public) on the preferable retrofit solution;
    ○
    The best solution for the private decision maker leads to a 43% reduction in CO2 emissions with a 3% increase in the hours of thermal discomfort;
    ○
    The best solution for the public decision maker provides a 39% reduction in CO2 emissions while maintaining the hours of discomfort unaltered.
  • In the case of scenario B (operation of the system with 100% outdoor air + inverter and automatic dampers), for the implementation of deep retrofit intervention solutions, the following can be said:
    ○
    The best deep retrofit solution is the same for both public and private decision makers;
    ○
    The best solution for both decision makers provides a 42% reduction in CO2 emissions and an 11% increase in discomfort hours.
Ultimately, the findings can inform broader guidelines for HVAC system retrofits in higher education facilities and similar building types facing the dual challenge of maintaining occupant health and reducing operational costs. By directly addressing the study’s larger objectives, these results demonstrate that integrating dynamic energy simulation with MOO and robust MCDM techniques can effectively balance infection control, energy efficiency, and economic viability. This comprehensive framework not only aids in identifying optimal retrofit interventions but also provides actionable insights for policymakers and industry stakeholders, guiding future HVAC retrofitting strategies in a post-pandemic context. Furthermore, the observed significant reductions in CO2 emissions combined with modest impacts on thermal comfort highlight the potential for targeted retrofits to reduce building energy consumption substantially.
Future research should explore the integration of renewable energy sources and advanced control strategies within this framework, as well as investigate scalability to other building types, to further enhance retrofit outcomes and support sustainable building practices.

Author Contributions

D.D.: conceptualization; methodology; software; validation; formal analysis; investigation; resources; data curation; writing—original draft; writing—review and editing; visualization; supervision; project administration; funding acquisition. F.M. (Federico Minelli): conceptualization; methodology; software; validation; formal analysis; investigation; resources; data curation; writing—original draft; writing—review and editing; visualization; supervision; project administration; funding acquisition. F.M. (Francesco Minichiello): conceptualization; methodology; software; validation; formal analysis; investigation; resources; data curation; writing—original draft; writing—review and editing; visualization; supervision; project administration; funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

No funding.

Data Availability Statement

Data and materials are available on request.

Acknowledgments

The authors gratefully acknowledge the Program for the Funding of Research at the University of Naples Federico II, 2024, typology A. Project code: 00011--ALTRI_CdA_80_2024_FRA_LineaA_SMARS. Project title: “SMArt and Resilient Schools”. Acronym: “SMARS”.

Conflicts of Interest

The authors declare no conflicts of interest.

References

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Energies 18 01526 g001
Figure 1. Flowchart of the implemented workflow.
Figure 1. Flowchart of the implemented workflow.
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Figure 2. AHP two-by-two comparisons, calculated weights (Wi,norm), and CR evaluation.
Figure 2. AHP two-by-two comparisons, calculated weights (Wi,norm), and CR evaluation.
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Figure 3. HVAC system layout for the case study university lecture room.
Figure 3. HVAC system layout for the case study university lecture room.
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Figure 4. Indoor air temperature during a typical summer day: measured vs. simulated values.
Figure 4. Indoor air temperature during a typical summer day: measured vs. simulated values.
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Figure 5. Comparison between the HVAC system energy consumption in scenarios A and B.
Figure 5. Comparison between the HVAC system energy consumption in scenarios A and B.
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Figure 6. MOO results for (A) scenario A and (B) scenario B.
Figure 6. MOO results for (A) scenario A and (B) scenario B.
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Table 1. Saaty’s judgement scale was used for a two-by-two criteria preference assessment [66].
Table 1. Saaty’s judgement scale was used for a two-by-two criteria preference assessment [66].
Intensity of ImportanceDefinitionDescription
1Equal importanceTwo criteria contribute equally
3Moderate importance of one over anotherModerate preference of the first criterion compared to the other one
5Essential or strong importanceStrong preference of the first criterion compared to the other one
7Very strong importanceVery strong preference of the first criterion compared to the other one
9Extreme importanceThe preference of the first criterion compared to the other one is the highest possible
2, 4, 6, 8Intermediate values between the two adjacent judgementsWhen compromise is needed
ReciprocalsWhen to a criterion “a” it is assigned one of the above reported judgements when related to a second criterion “b”, then to the second criterion “b” it is assigned the reciprocal value when related to the first criterion “a”.
Table 2. Pareto solutions for deep retrofit of scenario A considering the variables of the building-plant system for each optimal alternative and the Key Performance Indicators.
Table 2. Pareto solutions for deep retrofit of scenario A considering the variables of the building-plant system for each optimal alternative and the Key Performance Indicators.
IterationVariablesKPIsPMV
Cooling SetpointHeating SetpointArtificialLightingLighting Power DensityShading TypeRoof Glazing TypeThermal MassEERCOPCO2-eq EmissionsDiscomfortCostSummer PMVWinter PMV
[°C][°C] [W/m2][-] [-][-][-][kg/year][h][€][-][-]
325.819.2LED4.50.2 m louvresTriple–Low Emission-AirHeavyweight3.45.09566121727,7880.90.9
826.219.0LED4.50.6 m louvresTriple–Low Emission-ArgonHeavyweight3.83.49693119329,5900.3−0.6
3425.019.2Dimmable LED2.50.2 m louvresTriple–Low Emission-AirLightweight3.84.28945125927,9900.9−1.0
4026.219.4Dimmable LED3.00.6 m louvresTriple–Low Emission-KriptonLightweight5.05.09145123834,297−0.4−0.7
4727.019.0Dimmable LED4.00.6 m louvresTriple–Low Emission-ArgonHeavyweight3.84.29039124329,7920.2−0.6
5925.020.2LED3.50.4 m louvres + sidefinsElectronically tintable glazingHeavyweight5.03.815771113225,825−0.60.3
7726.220.6LED5.00.4 m louvresTriple–Low Emission-ArgonLightweight5.03.49595121029,1400.60.5
8227.019.4Dimmable LED3.50.2 m louvresElectronically tintable glazingHeavyweight3.44.68850128346,736−0.9−0.5
8526.219.0LED4.00.4 m louvres + sidefinsTriple–ArgonMedium weight5.04.69489123649,6870.4−0.4
14725.021.0Dimmable LED3.50.2 m louvres + sidefinsTriple–ArgonMedium weight3.85.08888127136,018−0.70.2
26026.619.4LED3.50.4 m louvresTriple–Low Emission-KriptonHeavyweight4.63.49833118934,0950.80.6
26426.819.4Dimmable LED4.50.2 m louvresTriple–Low Emission-ArgonHeavyweight3.85.08967124348,538−0.80.1
Table 3. Pareto solutions for deep retrofit of scenario B considering the variables of the building-plant system for each optimal alternative and the Key Performance Indicators.
Table 3. Pareto solutions for deep retrofit of scenario B considering the variables of the building-plant system for each optimal alternative and the Key Performance Indicators.
IterationVariablesKPIsPMV
Cooling SetpointHeating SetpointArtificialLightingLighting Power DensityShading TypeRoof Glazing TypeThermal MassEERCOPCO2-eq EmissionsDiscomfortCostSummer PMVWinter PMV
[°C][°C][-][W/m2][-][-][-][-][-][kg/year][h][€][-][-]
626.620.0Dimmable LED4.00.2 m louvres + sidefinsTriple–Low Emission-ArgonLightweight4.23.49499137423,6000.0−1.0
3025.820.6LED4.50.4 m louvres + sidefinsTriple–Low Emission-ArgonMedium weight4.23.416317121049,9770.60.5
3525.220.6Dimmable LED2.50.4 m louvres + sidefinsTriple–ArgonHeavyweight4.64.29416138730,2440.5−0.4
4725.219.4LED4.00.6 m louvresTriple–Low Emission-ArgonMedium weight53.410130132349,237−0.30.5
5725.019.0LED5.00.4 m louvresTriple–ArgonHeavyweight53.810020134028,6890.1−0.3
7326.620.4LED2.00.6 m louvresTriple–Low Emission-ArgonLightweight53.813328127729,4690.8−0.6
9526.219.2LED3.50.4 m louvresElectronically tintable glazingMedium weight4.23.89970135727,3370.5−0.2
13525.419.8Dimmable LED2.50.2 m louvres + sidefinsElectronically tintable glazingHeavyweight3.459377141328,441−0.81.0
66025.419.0LED4.50.2 m louvresTriple–Low Emission-AirMedium weight3.84.216317123247,2740.60.4
Table 4. Differences in ensemble rankings of the two stakeholders for scenario A. Cell colour reflects the value of D (the lowest value is red coloured and the highest value is green coloured).
Table 4. Differences in ensemble rankings of the two stakeholders for scenario A. Cell colour reflects the value of D (the lowest value is red coloured and the highest value is green coloured).
IterationDM1–CollectivityDM2-Private
RankDRankD
3321−2
8615−1
341−232
405−484
472−565
5912102−10
774040
827−292
8511−1121
14710−2112
260927−2
2648−2102
Table 5. Differences in ensemble rankings of the two stakeholders for scenario B. Cell colour reflects the value of D (the lowest value is red coloured and the highest value is green coloured).
Table 5. Differences in ensemble rankings of the two stakeholders for scenario B. Cell colour reflects the value of D (the lowest value is red coloured and the highest value is green coloured).
IterationDM1–CollectivityDM2-Private
RankDRankD
61010
308−191
352−464
476−282
57523−2
73725−2
95422−2
1353−141
660927−2
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D’Agostino, D.; Minelli, F.; Minichiello, F. HVAC System Energy Retrofit for a University Lecture Room Considering Private and Public Interests. Energies 2025, 18, 1526. https://doi.org/10.3390/en18061526

AMA Style

D’Agostino D, Minelli F, Minichiello F. HVAC System Energy Retrofit for a University Lecture Room Considering Private and Public Interests. Energies. 2025; 18(6):1526. https://doi.org/10.3390/en18061526

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D’Agostino, Diana, Federico Minelli, and Francesco Minichiello. 2025. "HVAC System Energy Retrofit for a University Lecture Room Considering Private and Public Interests" Energies 18, no. 6: 1526. https://doi.org/10.3390/en18061526

APA Style

D’Agostino, D., Minelli, F., & Minichiello, F. (2025). HVAC System Energy Retrofit for a University Lecture Room Considering Private and Public Interests. Energies, 18(6), 1526. https://doi.org/10.3390/en18061526

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