Analysis of the Impact of Funding Policies for the Energy Refurbishment of Buildings Using Dynamic Simulations

Abstract

Dynamic simulations can accurately estimate the thermal demands for space heating and cooling in buildings, as well as the energy and economic performance of specific energy refurbishment actions. This study aims to evaluate the energy and economic savings resulting from the adoption of particular energy measures applied to a cluster of residential condominium buildings, also considering some possible Italian funding policies. To this scope, dynamic simulation models of several buildings with different features in terms of geometry, shape, and thermo-physical properties are considered. The selected buildings are representative of the most common buildings in the city of Naples, Southern Italy. Two scenarios regarding the possible penetration of the refurbishment actions are considered: the “25% scenario”, where 25% of buildings in the Naples municipality adopt the selected measures, and the “100% scenario”, where all buildings adopt such energy refurbishment actions. The results of the simulations, reported over different time periods, compare the economic, energy, and environmental benefits of the specific energy measures. This study evaluates the replacement of conventional natural gas-fired boilers with natural gas-fired condensing boilers, as well as the use of thermal insulation on the external walls of the buildings. The primary energy demand for space heating decreased by 28% when the proposed energy measures were implemented in all buildings of the Naples municipality.

1. Introduction

With the growing needs of emerging economies, global building stock is projected to increase by more than 230 billion square meters by 2060, with over 50% growth in building energy demand [1]. Therefore, reducing carbon emissions in the residential building sector holds enormous potential for decarbonization and achieving the global 1.5 °C target [2]. Renovating existing buildings could decrease the total energy consumption of the European Union (EU) by 5–6% and lower CO₂ emissions by around 5% [3]. Approximately 60–70% of the total energy consumption of residential buildings, about 180 kWh/m² [4], is due to space heating and cooling [5]. This high consumption is often due to the poor thermos physical properties of the building envelope, characterized by high thermal transmittance and low thermal inertia [6].

One of the main goals of the EU “Green Deal” policy is the reduction in the primary energy demand for space heating systems [7]. In this context, the “Fit for 55” package aims to cut CO₂ emissions by 55% relative to 1990 levels by 2030. Consequently, energy actions based on the adoption of thermal insulation materials for building external walls are very common [8]. Another key energy measure involves replacing inefficient gas-fired boilers, which typically have a thermal efficiency ranging from 75% to 85%, with modern condensing boilers. These boilers achieve thermal efficiencies greater than 100% by utilizing both sensible and latent heat through the condensation of exhaust gases [9]. Additionally, the Energy Performance of Buildings Directive (EPBD) of March 2023 emphasizes the need for green buildings to meet stringent energy performance standards. This directive mandates significant improvements in building energy efficiency to align with the EU climate targets. Various incentives and funding mechanisms are available to support these upgrades, ensuring compliance with the EPBD requirements and promoting widespread adoption of energy-efficient practices [3].

In Italy, the Superbonus 110% is one of the most significant incentives for building retrofit projects. This program allows homeowners to deduct 110% of the costs for energy efficiency improvements, including insulation, heating system replacements, and the installation of solar panels. The Superbonus stands out because it covers not only the full cost of the interventions but also the related expenses, such as feasibility studies and technical reports, effectively making the upgrades cost-free for the beneficiaries. This incentive has been instrumental in accelerating the adoption of energy-saving measures across Italy, promoting energy-efficient practices at a large scale. Due to its generous nature and comprehensive coverage, it is widely regarded as the most effective incentive for achieving the nation’s energy efficiency targets [10]. Other incentives exist, such as Ecobonus and Sismabonus, but the Superbonus 110% is unparalleled in its impact and scope [11].

Energy renovation of the residential building envelope can notably reduce the building‘s primary energy demand for space heating and cooling [12]. It should be noted that the EU sets the directives and overall framework for energy efficiency improvements, while the implementation and provision of incentives are managed by individual member states. National governments are responsible for developing specific measures and funding programs to meet the EU targets within their own contexts. As a result, EU Countries have increasingly provided public incentives to improve building stock renovation. An interesting review of EU energy efficiency policies for buildings is presented in ref. [13].

Several techniques to enhance the energy performance of buildings are documented in the literature [14,15]. For instance, using 8 cm of thermal insulation made of polystyrene foam with added graphite (Expanded Sintered Polystyrene, EPS) in an external wall based on reinforced concrete and bricks (0.981 W/m²K) can reduce wall transmittance to 0.21 W/m²K, achieving a 51.8% reduction in primary energy demand [10]. In ref. [16], four scenarios are proposed with different energy efficiency measures and HVAC systems for block apartment buildings in Prishtina, Kosovo, showing a reduction in heating energy demand by 116.7 MWh/year, natural gas demand (gas boilers partially replaced by heat pumps) by 243.49 kWh/year, and 86% mitigation of carbon dioxide emissions after implementing the measures. Caputo et al. [17] presented some simulations of two envelope scenarios in Milan (internal and external insulation) using Integrated Environmental Solutions: Virtual Environment (IESVE) software, indicating that the external insulation scenario provided a slightly lower peak thermal flow rate. Cholewa et al. [18] performed energy audits of 11 comparable multifamily buildings in Poland, revealing that rebalancing heating systems and using thermal insulation materials could effectively reduce energy consumption.

To enhance the thermo-physical properties of building facades, polyvinyl chloride-coated polyester fabric material can reduce space heating energy demand by about 9.8% [19]. An innovative insulating plaster material made from corn production waste and natural hydraulic lime of Wasselonne can decrease thermal energy losses by 20–40% in historic buildings [20]. Retrofit actions on the envelope of residential buildings across various locations were evaluated by Mauri et al. [21]. Results proved that the insulation material on internal walls can improve building energy performance.

Dynamic simulation models are widely adopted to assess building energy performance and related efficiency actions. These models optimize overall system performance and evaluate real-time system operation [22]. Among the tools available, the TRaNsient SYstem Simulation (TRNSYS) software is commonly used by researchers. TRNSYS, based on a library of validated components, produces highly reliable results [23]. Dynamic models not only simulate the real properties of the building envelope but also model HVAC systems, providing robust and accurate evaluations of building energy consumption and proposed energy actions.

Many studies in the literature on residential building renovation highlight the importance of accurately detecting reductions in energy consumption and actual economic savings for users. In this context, this paper aims to demonstrate the real advantages of using a dynamic simulation approach when a large number of buildings implement energy refurbishment actions, also considering possible economic incentives. To this end, detailed models are presented to evaluate the economic and energy savings from the adoption of particular energy measures related to building envelopes and the replacement of traditional boilers.

These models are developed for various building geometries and shapes using the TRNSYS environment, a well-known tool in the research community. The ultimate goal is to estimate the energy, environmental, and economic savings associated with the application of such measures for two penetration scenarios. The results of this work can be valuable for system designers and end users, providing an accurate assessment of the energy and economic performance of different thermal insulation materials for external walls and novel heating systems.

This study does not investigate the performance in the summer period due to the reduction in cooling energy demand after the energy retrofit of the condominium buildings. In the reference scenario, the condominium buildings under consideration are not equipped with cooling systems, and the primary focus of this study is on reducing energy consumption during the heating season. Therefore, the cooling season was not included in the current analysis in line with the scope and objectives of this work.

2. Materials and Methods

In this section, the method employed to carry out this study is presented. The Section 3 details crucial aspects of the dynamic simulation model used for the investigated buildings. This model evaluates the heating load of each building, considering different hourly trends of heat gain and heating plant activation. The dynamic simulations are implemented in the TRNSYS environment with a time step of 0.05 h. The building simulation model is based on TRNSYS Type56, a well-known type commonly used for building dynamic simulations [24]. This model is applied to buildings with various geometries and shapes in the Naples municipality, as described in the Section 4.

The proposed energy refurbishment scenarios compared with the reference system involve boiler replacement and the use of thermal insulation materials. The comparison between the reference scenario and the retrofit scenario is possible thanks to a standardized process. Both the buildings (ante and post retrofit) are identical in terms of geometry, orientation, weather location, use, and boundary conditions, with fixed thermophysical features and energy parameters. Therefore, the energy class of the retrofitted building is identified by evaluating the primary energy consumption, which is compared with that of the reference building.

The methodology of this research is very detailed, and the approach is not overly simplistic. In fact, compared to the widely used tools that evaluate the energy class of buildings based on the semi-stationary approach, the presented method is based on a dynamic simulation model of the whole building. Therefore, all the main inputs and outputs of the system are evaluated at each time step of the simulation for all days of the winter season, overcoming the limitations of the semi-stationary models widely adopted in commercial applications for their simplified approach.

The results of the dynamic simulations detailed in the Section 5 are discussed for the entire heating season, considering different penetration scenarios of the considered actions for 25% and 100% of the buildings in the Naples municipality. In this study, two scenarios—25% and 100% transformation—were chosen to represent partial and full adoptions of energy efficiency measures. The 25% scenario models a conservative approach, while the 100% scenario reflects the maximum potential energy savings if all eligible buildings are upgraded. This contrast offers a clear understanding of the range of possible outcomes. The methodology, however, is flexible and can accommodate other transformation percentages depending on policy goals or scenarios.

The energy refurbishment actions are determined using the same criterion previously adopted for the Superbonus incentive, an economic funding course in Italy up to 2023, which covers the total cost of refurbishment actions. The Superbonus incentive applies to various building types, including condominiums, private homes, and public buildings. Key measures include thermal insulation and upgrading outdated heating systems to more efficient ones. While insulation is effective, it involves high capital costs and long payback periods. In contrast, replacing heating systems results in significant energy savings. Another potential measure not addressed in this study is installing thermostatic valves in radiator-based systems, which also offers energy-saving benefits. To obtain this incentive, the energy refurbishment must improve the building energy class by at least two levels [25]. The energy class is determined by comparing the primary energy consumption of the existing building with that of a reference building, as specified in the legislation [10]. Energy, economic, and environmental analyses were also performed for all the considered building geometries.

3. Model

The dynamic simulation model is developed using TRNSYS, a highly reliable and accurate dynamic simulation tool commonly employed to simulate various end-use buildings, including residential [26] and touristic buildings [27]. To validate the in-house building simulation models [28], TRNSYS is considered by the scientific community as a benchmark tool. Type 56, a component of TRNSYS, produces highly reliable outputs for conducting dynamic simulations on building–plant systems and buildings [26].

In this study, Type 56 is used to dynamically evaluate the heating loads of all buildings. The validation of Type 56 is presented in ref. [29]. The building models account for 3D geometry configured using the Google SketchUp TRNSYS3d plug-in [30], as well as the primary factors affecting building load. The geometries of the buildings investigated in this work are depicted in Figure 1.

To accurately simulate the buildings, users must specify the thermophysical properties of the building envelope and the weather zone, accounting for the effects of solar radiation, ambient temperature, and humidity. Additionally, detailed information on internal gains from machinery, lighting, and occupants must be provided. The building physics and details about the energy systems simulation models are documented in ref. [23]. The process for using both Type 56 and SketchUp is described in ref. [31]. For the sake of brevity, the detailed description of the building model development is omitted here. However, it is important to emphasize that the model is highly accurate and incorporates factors such as building orientation and the impact of surrounding shading from neighboring structures.

4. Case Study

The investigated case study regards residential building blocks located in Naples, Southern Italy, with the typical thermophysical proprieties of residential buildings constructed during the 1960s and 1980s, as shown in

Table 1. Features of opaque and transparent components.

Building	Component	U [W/m2K]
1 (external tuff walls)	External tuff wall	1.169
	Single-glazed wooden windows	5.35
	External roof	1.268
	Adjacent ceiling	1.445
	Ground floor	1.322
2 (external empty box walls)	External empty box wall	1.135
	Aluminium double-glazed windows without thermal separation	2.82
	External roof	1.268
	Adjacent ceiling	1.445
	Ground floor	1.322

. Specifically, this table identifies two types of buildings, referred to as Building 1 and Building 2. Building 1 features external tuff walls and single-glazed windows with wooden frames, while Building 2 has external empty box walls and double-glazed windows with aluminum frames without thermal break. This distinction arises because buildings with similar shapes and geometries are sometimes constructed using different opaque elements. According to the 2001 ISTAT data, the buildings in the municipality represent a heterogeneous building heritage regarding the construction period. ISTAT (Istituto Nazionale di Statistica) is the Italian National Institute of Statistics, responsible for collecting and analyzing data related to various aspects of Italian society, including demographics, economy, and housing. These data are essential for understanding the characteristics of the building stock in different regions [32]. The thermo-igrometric features of the stratigraphy of the buildings, both in the case of external tuff walls and empty box walls, have been reported in

Table 2. Stratigraphy of the external tuff walls.

Layer	Thickness [M]	Conductivity [KJ/HMK]	Specific Heat Capacity [KJ/KGK]	Density [kg/M3]	U [W/M2K]
Lime and gypsum plaster	0.020	2.520	1.000	1400	1.169
Tuff masonry	0.400	2.268	0.700	1500
Cement, sand, lime plaster	0.020	3.240	0.910	1800

and

Table 3. Stratigraphy of the empty box walls.

Layer	Thickness [m]	Conductivity [kj/hmk]	Specific Heat Capacity [kj/kgk]	Density [kg/m3]	U [w/m2k]
lime and gypsum plaster	0.020	2.520	1.000	1400	1.135
hollow bricks	0.080	1.476	0.840	1600
air cavity		R = 0.05 hm2K/kJ
hollow bricks	0.120	1.476	0.840	1600
cement, sand, lime plaster	0.020	3.240	0.910	1800

, respectively. Thermal insulation was applied to both the outside and inside surfaces. The simulations performed take into account the heat absorbed by the walls, as the building model considers the thermal mass of the walls themselves. The walls are modeled according to the transfer function relationships of Mitalas and Arseneault [33,34,35]. The dynamic energy balance of each wall accounts for the radiation heat flux absorbed at both the inside and outside surfaces, the radiative heat transfer with all other surfaces within the zone, and the conduction and convection heat fluxes.

According to ISTAT data, 34,206 buildings belong to the Naples municipality. However, the analysis was conducted only for 2940 buildings, representing 21% of condominium buildings with four or more floors and equipped with centralized heating systems. It was assumed that these centralized heating systems were based on traditional natural gas-fired boilers with a generation efficiency (η_th) of 0.80. Seven different building typologies were identified in terms of shape, orientation, and size across different neighborhoods/municipalities (Figure 1). These typologies include block building (A), closed courtyard building (B), open courtyard building (C), four-floor line building (D), six-floor line building (E), eight-floor tower building (F), and ten-floor tower building (G). The features of the investigated buildings, including building height, volume, and area for each building shape (A to G), are summarized in

Table 4. Features of investigated buildings.

	A	B	C	D	E	F	G
Height [m]	18	15	15	12	18	24	30
Volume [m3]	4608	16,500	16,005	11,340	11,520	4080	17,280
Area [m2]	256	1100	1067	945	640	170	576
Number of floors	6	5	5	4	6	8	10
Number of apartments per floor	3	14	10	10	7	2	5
Window area [m2]	210.54	311.40	539.72	141.12	320.16	241.92	355.20
Shape	block	closed courtyard	open courtyard	line	line	tower	tower

. This table also reports the number of floors, the number of apartments per floor, and the window area.

Table 5. Number of buildings per each constructive type.

Type	A1	B1	C2	D1	D2	E1	E2	F1	F2	G1	G2	TOT
Number	876	512	374	54	158	125	420	59	212	24	126	2940
Percentage	30%	17%	13%	2%	5%	4%	14%	2%	7%	1%	4%

shows the number of buildings per each type considered.

According to data from the Italian Institute of Statistics (ISTAT) for the year 2020, the considered building stock houses 948,850 people. To define user habits, the following assumptions are considered: all adults go to work every weekday, and children and young people attend school or university. Occupation profiles are created for each category of people (elderly, adults, children, teenagers, and young adults), considering different habits for weekdays (Monday to Friday), Saturdays, and Sundays. Based on these occupation profiles, the total daily occupancy profile of building users is calculated as the average occupancy profile. An example of the occupancy profile for weekdays (Monday to Friday) is shown in Figure 2, whereas

Table 6. Distribution of people inside the apartments.

	Age Classes (Years)	Number (-)	Amount on Total Users (%)
Elderly	65–100+	192,535	20.29
Adults	35–64	400,256	42.18
Children and Teenagers	0–19	186,746	19.68
Youngs	20–34	169,313	17.84

shows the distribution of people inside the apartments.

It is important to mention that the occupancy profiles developed for this study represent typical usage patterns of residential buildings. Specifically, three distinct occupancy profiles based on national statistical data and standard occupancy schedules outlined in ref. [10] are considered. These occupancy profiles are modeled by accounting for variables such as the number of occupants, their daily schedules, internal heat gains from occupants and appliances, and ventilation rates. This approach only includes residential buildings; therefore, it does not account for the energy usage in other buildings where residents may spend time when they are not at home, such as workplaces, schools, or commercial spaces. As a result, any potential increase in energy consumption in these non-residential buildings due to occupant presence has not been included in the analysis, which is strictly limited to the residential building stock.

The heat gains due to the diverse appliances considered are reported in

Table 7. Power and heat gains due to the diverse appliances considered.

	Power (kW)	Heat Gain (kW)	Radiation Fraction (%)	Convection Fraction (%)
Fridge	0.040	0.040	0	100
Dishwasher	1.820	0.364	51	34
Oven	0.870	0.522	14	49
Cooktop	1.500	0.900	24	16
TV	0.240	0.240	40	60
PC 3.5 GHz processor 16 GB ram	0.090	0.090	10	90
Laptop	0.059	0.059	25	75
Washing machine	1.270	0.254	40	60

. Each heat gain has a specific schedule that varies on weekdays, Saturdays, and Sundays.

Regarding the lighting, considering that the average surface area of the apartment is about 90 m², it was assumed that the living room and kitchen were equipped with five LED lights of 14 W each, and the other rooms were equipped with six fluorescent lights for a total of 82 W. For all types of users, the lights are turned on from 7:00 to 23:30 if the total solar radiation on the horizontal surface is lower than 120 W/m², and in particular:

-

For 60% of the total lights, if four people are in the apartment;

-

For 50% of the total lights, if three people are in the apartment;

-

For 40% of the total lights, if two people are in the apartment;

-

For 20% of the total lights, if one person is in the apartment.

It is important to point out that for all residential units, the lights are switched off if no user is in the apartment.

Naples belongs to climatic zone C according to the Italian Presidential Decree DPR 74/2013 [2]; therefore, the heating system is switched on from November 15th to March 31st. The centralized heating system is activated from 07:00 to 11:00 and from 17:00 to 21:00. An indoor set point temperature of 20 °C is considered for each thermal zone of the building, which is equipped with a suitable plant featured by a thermal flow rate of 80 W/m².

A constant infiltration rate equal to 0.6 l/h was assumed, and a ventilation rate equal to 2 l/h was set to simulate the window opening in the early morning hours, from 07:00 to 08:00.

The energy measures proposed in this work are the ones successfully implemented by the Italian funding policy, known as “Superbonus 110%“ [10], namely:

• The natural gas-fired traditional boilers are replaced by natural gas-fired condensing boilers, with a generation rated efficiency η_th equal to 0.98;
• The external tuff walls are insulated using 0.1 m of rock wool;
• The external empty box walls are insulated by 0.06 m of rigid polyurethane foam.

The condensing boilers are modeled with a thermal efficiency of 0.98, reflecting the typical performance of modern condensing boilers under standard operating conditions. This efficiency aligns with the technical requirements of the Superbonus 110% incentive, which promotes the adoption of high-efficiency heating systems to achieve significant energy savings [10]. Other boiler efficiencies were not considered, as they either do not meet the incentive criteria or are not commonly used in the context of the residential buildings analyzed. The thermophysical properties of the thermal insulation in the proposed system are shown in

Table 8. Thermophysical properties of the thermal insulations in the proposed system.

Thermal Insulations	Thickness [m]	Conductivity [kJ/hmK]	Specific Heat [kJ/kgK]	Density [kg/m3]	Cost [€/m2]
Rock wool	0.100	0.126	1.03	100	66.34
Rigid polyurethane foam	0.06	0.0792	1.453	36	68.64

.

The thicknesses of the insulation materials of the external walls are selected in order to reduce the total transmittance of the external walls according to the limit values expected for the Superbonus incentive (

Table 9. Thermal conductance limits for the building’s external walls according to the Italian Legislation.

Solution Proposed	Technical Requirements for the Solution Proposed
Thermal insulation of the walls	Weather zone C	U ≤ 0.30 W/m2K
	Weather zone D	U ≤ 0.26 W/m2K
	Weather zone E	U ≤ 0.23 W/m2K

). The total transmittance of the external tuff wall and the external empty box wall by the selected thicknesses of the insulation materials decreases to 0.268 W/m²K and 0.275 W/m²K, respectively.

5. Results

The simulation results obtained in the TRNSYS environment for the investigated buildings are discussed in this section. TRNSYS software allows for the calculation of various parameter trends over different time bases (yearly, monthly, weekly, daily, hourly). In this work, a time step of 0.05 h is selected. The trend of the total heat transfer rate for a typical winter day will be analyzed by summing up all the heat transfer rates related to the considered building geometries. Monthly results for the thermal energy demand and primary energy of all buildings obtained from dynamic simulations will be described for both the reference and proposed energy refurbishment scenarios.

Yearly results from the dynamic simulations will compare the reference system and the proposed energy refurbishment scenario in terms of CO₂ emissions, primary energy, and natural gas cost.

The results pertain to each type of building, i.e., individual buildings with geometries/shapes representing types A to G. However, the analysis will also cover the entire building stock, i.e., all 2940 buildings representing types A to G in the Naples municipality, to evaluate the effect of the proposed energy measures according to two penetration scenarios.

5.1. Hourly Results

Figure 3 shows the operation of the heating system for a typical winter day by considering the sum of the heat transfer rates of all the buildings with the geometry A to G in the reference and proposed systems. Therefore, the heat transfer rates were calculated considering the heat transfer rate of the single building belonging to a specific geometry and the number of buildings belonging to this geometry. According to the assumed schedule of the heating system, the heat transfer rate is no null from 07:00 to 11:00 and from 17:00 to 21:00. The heat transfer rate reaches maximum values when the temperatures are coldest, i.e., in the morning from 07:00 to 8:00 and when the majority of the population is at home, i.e., at 17:00, due to the simultaneous switching on of the heating systems. The maximum value corresponds to a heat transfer rate of 827 MW. The difference in heat transfer rates between the reference and proposed system is due to the energy measures implemented in all the considered buildings based on the use of thermal insulation materials. This average difference is about 40 MW, while the maximum difference identified between the reference and proposed system is 131 MW, detected after 17:00.

5.2. Monthly Results

Figure 4 shows the monthly results for the total thermal energy demand of all buildings representing geometries/shapes A to G in both the reference and proposed systems. These monthly results were calculated by considering the thermal energy demand of a single building belonging to a specific geometry and multiplying it by the number of buildings of that geometry.

In the reference system, January has the highest thermal energy demand, at 137.3 GWh, followed by December at 129.1 GWh. In the proposed system, the monthly thermal energy demands decrease, with January still having the highest demand but reduced to 118.2 GWh. Thus, thermal insulation reduces the thermal energy demand of the buildings by 14% in January.

5.3. Yearly Results

In this section, the yearly thermal energy demand for each investigated building geometry is reported. In Figure 5, the building is identified by a letter and a number. The letter indicates the shape/geometry of the building, while the number indicates the construction type: buildings marked with the number 1 have external tuff walls, and buildings marked with the number 2 have external empty box walls (Table 1).

Figure 5 shows that building B1 has a greater yearly thermal energy demand than other buildings of different shapes. This can be explained by the shape of building B1, a closed courtyard building (Figure 1) with much higher shaded areas compared to other buildings that are more exposed to solar radiation.

Conversely, the buildings with the lowest thermal energy demand are A1, F1, and F2, which are block buildings and tower buildings, respectively. These buildings have a smaller number of apartments and, therefore, a lower thermal energy demand for space heating. This is due to the assumptions of the developed model, which considers one apartment per floor for these buildings.

The proposed system involves the use of thermal insulation materials on the external walls and the replacement of the condensing boiler with one of 0.98 rated efficiency. As a result, the thermal energy demands and related primary energies for all buildings decrease, as shown in both graphs in Figure 5. The average primary energy savings (PES) achieved for all buildings range from 21% to 37% (

Table 10. Primary energy savings between the reference and proposed system for the buildings considered.

PES [%]	A1	B1	C2	D1	D2	E1	E2	F1	F2	G1	G2
	28	31	24	21	21	25	24	37	36	28	30

). The proposed energy measures result in the lowest primary energy savings of 21% observed for the four-floor line buildings, D1 and D2. Conversely, the highest primary energy savings of about 37% are observed for the tower buildings, F1 and F2.

The highest primary energy savings at about 37% observed for the tower buildings F1 and F2 are mostly due to geometrical features. Although these buildings have lower absolute thermal and primary energy demands in the reference system due to their smaller number of apartments and overall floor areas, their higher surface-to-volume (S/V) ratio means that a greater proportion of their heat loss occurs through the building envelope. As a result, the application of thermal insulation has a more significant relative impact on reducing their total energy consumption. The insulation effectively reduces heat losses through the extensive external surfaces, leading to higher percentage savings compared to other building typologies with lower S/V ratios. Therefore, despite lower absolute energy demands, the relative weight of the insulation on the total consumption is higher for buildings F1 and F2 due to their geometric characteristics. Once the primary energies were calculated, the CO₂ emissions for each building geometry in both the reference and proposed systems were estimated. An emission factor of 0.20 kgCO₂/kWh_p was used for this calculation. Additionally, the cost of natural gas per unit area for each building geometry was evaluated, assuming a unit cost of Euro 0.70/Sm³.

For all geometries, the average yearly avoided CO₂ emissions were 3.85 kgCO₂/m² (Figure 6), and the average yearly cost saving was Euro 2/m² (Figure 7).

The economic evaluation of the adopted energy measures, including the use of thermal insulation materials and the replacement of boilers, is performed. The total capital cost of the energy measures considers the specific cost per m² of the thermal insultation materials, the capital cost of the new boilers installed in the considered buildings, and the technical costs due to surveys, feasibility studies, preliminary, final, and executive designs, as well as works management. Specifically, for the thermal insulation energy measure, the technical costs amount to about 25% of the total cost, while for boiler replacement, the technical costs amount to about 25% of the total cost. According to the prices suggested for public investments in the Campania region [36], the specific costs for both thermal insulation materials are reported in Table 8. The capital costs of the new boilers are determined based on the values suggested in ref. [36] as a function of the rated thermal flow rate of the heating system. The total capital cost and Simple Payback (SPB) period for each building for both energy measures are reported in

Table 11. Economic analysis: economic savings and simple payback for each building geometry.

	Thermal Insulation on the External Walls				Boiler Replacement				Yearly Economic Saving and Payback Period of Both Energy Measures
	I0 [€]	Technical Costs [€]	Total Capital Cost [€]	Limit Cost [€]	I0 [€]	Technical Costs [€]	Total Capital Cost [€]	Limit Cost [€]	ΔC [€/y]	SPB [y]
A1	76,424	19,106	95,530	540,000	11,158	1674	12,831	270,000	2175	49.8
B1	229,271	57,318	286,589	2,100,000	22,724	3409	26,133	1,050,000	13,831	22.6
C2	205,920	51,480	257,400	1,500,000	22,724	3409	26,133	750,000	6260	45.3
D1	124,188	31,047	155,236	1,200,000	21,214	3182	24,396	600,000	4564	39.4
D2	128,494	32,124	160,618	1,200,000	21,214	3182	24,396	600,000	4474	41.4
E1	133,741	33,435	167,177	1,260,000	21,214	3182	24,396	630,000	4522	42.4
E2	138,378	34,595	172,973	1,260,000	21,214	3182	24,396	630,000	4418	44.7
F1	85,977	21,494	107,471	480,000	11,158	1674	12,831	240,000	2207	54.5
F2	88,957	22,239	111,197	480,000	11,158	1674	12,831	240,000	2131	58.2
G1	191,059	47,765	238,824	1,500,000	28,564	4285	32,849	750,000	6852	39.6
G2	197,683	49,421	247,104	1,500,000	28,564	4285	32,849	750,000	7409	37.8

.

The best SPB, equal to 22 years, is achieved for building B1, reflecting the reduction in thermal energy consumption achieved for this building geometry. Note that the achieved SPB values are quite high, but the values are common and comparable with those found in the literature when such energy measures are implemented [36]. Additionally, Table 11 compares the capital costs of each energy measure with the limit costs assumed for the Superbonus 110% incentive [36]. For all buildings, the capital costs are significantly lower than these limits. This confirms that the application of the proposed energy efficiency measures using the Superbonus 110% incentive can be considered at zero cost from the user perspective, with significant annual economic savings in terms of natural gas costs.

The energy and environmental results of two scenarios concerning the penetration of the considered actions are also evaluated. The “25% scenario” involves the adoption of the measures for 25% of the residential condominium buildings in the Naples municipality (735 out of 2940 buildings) that have four or more floors and are equipped with centralized heating systems. Conversely, the “100% scenario” involves the adoption of the considered measures for all the residential condominium buildings in the Naples municipality (2940 buildings) with four or more floors and centralized heating systems.

The total primary energy consumption of all 2940 buildings is 809 GWh/year. If the “25% scenario” is implemented, the primary energy demand for space heating decreases by 7% (

Table 12. The penetration of the measures for 25% and 100% of the buildings belonging to the Naples municipality.

Scenario	PERS [GWh/y]	PEPS [GWh/y]	PES [GWh/y]	PES [%]	CO2,RS [kt/y]	CO2,PS [kt/y]	∆CO2 [kt/y]
25%	809	753	56	7	162	151	11
100%	809	585	224	28	162	117	45

). For the “100% scenario”, the primary energy demand for space heating decreases by 28% (Table 12). The avoided CO₂ emissions are 11 kt/year for the “25% scenario” and 45 kt/year for the “100% scenario”.

6. Conclusions

This study presents an energy, environmental, and cost analysis studying the reduction in thermal energy demand for residential condominium buildings following the implementation of specific energy measures. This study focuses on 2940 buildings located in Naples, Southern Italy, all equipped with centralized heating systems based on traditional boilers. Seven common building geometries/shapes were studied, identifying the number of buildings for each geometry to generalize the results.

The building geometries are defined using the Google SketchUp TRNSYS3d plug-in, and the dynamic energy simulations are performed in the TRNSYS environment. Type 56 of TRNSYS is used to simulate the buildings, and its outputs are considered extremely reliable for dynamic simulations of building and building–plant systems.

The results show that the primary energy consumption of the current heating systems totals 809 GWh/year. The energy-saving measures considered—thermal insulation for external walls and the replacement of boilers with condensing systems—were simulated in two adoption scenarios: 25% and 100%. In the full adoption scenario, primary energy savings reach 224 GWh/year (28.0%), and CO₂ emissions are reduced by 45 kt/year. The annual economic savings for users is estimated at 2.00 €/m², while the payback period aligns with the literature findings. However, without financial incentives, these energy efficiency measures are not economically viable. The Superbonus 110% makes the proposed measures cost-free for users, resulting in significant natural gas cost savings.

This study is limited to energy consumption during the heating season, excluding cooling demand, which is crucial given Italy’s high summer temperatures. Future research should assess cooling energy demand to provide a more comprehensive analysis. Additionally, this study only focuses on residential buildings, leaving out non-residential sectors such as offices and schools, which could benefit from similar measures. Expanding to include these sectors is essential for future studies. Moreover, while the analysis centers on the Superbonus 110% incentive, future research should explore a wider array of incentives and funding mechanisms. The integration of photovoltaic systems and Renewable Energy Communities presents an opportunity to further enhance energy savings and sustainability, which should be explored in subsequent work.