Achieving Maximum Smart Readiness Indicator Scores: A Financial Analysis with an In-Depth Feasibility Study in Non-Ideal Market Conditions

Abstract

For European competitiveness, energy efficiency must be increased. An important part of energy efficiency depends on an efficient building stock—the sector with the greatest potential for energy savings, as more than a third of all primary energy is consumed in buildings. A new instrument, the smart readiness indicator (SRI), is being prepared to accelerate the implementation of smart solutions in buildings and establish a market that would require and accelerate the implementation of such solutions. In this paper, we examine how the SRI score of a shopping center (with an already relatively advanced automation system) changes if we perform an energy optimization worth approximately 6.6 million EUR. As all the upgrades suggested by the SRI methodology cannot be implemented, this paper is the first of its kind to define the maximum feasible SRI score. The necessary measures are elaborated comprehensively, analyzed, and evaluated both technically and financially (IRR, ROI, and payback time). This type of approach is suitable for less developed EU markets without smart grids, DSM, and predictive functions.

1. Introduction

Globally, the consumption of buildings represents 30% of final energy [1]. The United Nations project that 66% of the world population will live in cities by the year 2050. Many of the world’s resources are concentrated in cities; they consume 75% of the total energy. This perpetual energy consumption generates nearly 80% of the greenhouse gases that have unfathomable adverse effects on the environment [2].

Because of the European Union’s (EU) commitment to a sustainable, competitive, secure, and decarbonized energy system by 2050, its building stock (which generates up to 36% of all CO₂ emissions) needs a complete transformation [3]. Therefore, the member states’ efforts are expected to meet the energy goals by 2050. The Green Deal wants to accelerate energy security, energy efficiency, decarbonization, research, innovation, and competitiveness [4].

In the EU, 75% of buildings are still energy-inefficient [5]. While this represents a large burden, it is a significant opportunity. Therefore, the question arises of how to speed up the process of energy renovations in the EU. The Energy Performance of Buildings Directive of 2018 presented a solution, the smart readiness indicator (SRI) [3]. The SRI should be used to measure the capacity of buildings to use information and communication technologies (ICT) and electronic systems, with which the operation of buildings would be adapted to the needs of residents and the network, and the energy and overall building efficiency would be improved. The smart systems readiness indicator should increase building owners’ and occupants’ awareness of the value of building automation and electronic monitoring of technical building systems and instill confidence in the occupants about the savings to be achieved with these new, improved features [3]. In the last 10–15 years, building automation has gained much recognition as a solution that raises comfort and, simultaneously, lowers energy consumption. Building automation and electronic monitoring are defined as technical building systems that are proven and effective substitutes for inspections, especially for large systems. The great potential for energy savings is interesting for customers and businesses [6].

The SRI is intended to measure the building’s capability to adapt to grids and the occupants’ needs through electronic systems and ICT, and to evaluate the building’s overall energy performance [7]. The new indicator aims to establish a common framework to promote the implementation of smart buildings. As a result, energy efficiency will be increased, energy consumption will be reduced, and new jobs will be provided [8]. In addition, a market of providers and solution seekers will emerge. Because of the SRI, investors and building users will be sufficiently informed or educated to look for energy-efficient solutions, aligned with the European energy policies. We are speaking of a “market pull” and a “market push” effect that could transform the energy market [9]. Some authors claim that SRI implementation should also consider the integrated use of data within a larger context of energy saving achieved via renovations [10]. Others believe it should be linked to the energy performance certificates (EPC)—thus indicating that alone, it loses its effectiveness [11]. However, the SRI is one of the few tools that should speed up the process of the circular economy and energy optimizations in buildings. The tools Levels [12] and the Digital Product Passport [13] need mentioning. Another tool proposed for energy consumption is IBACSA [14].

The feasibility and strategic relevance of the smart readiness indicator (SRI) implementation must be examined through a broader macroeconomic and geopolitical lens. While energy efficiency is often discussed in terms of environmental sustainability or building-level optimization, it also serves a vital economic function in many EU member states, particularly as a compensatory mechanism for limited fiscal and monetary capacity when compared to more centralized and agile economies such as the United States. Since the 2008 global financial crisis, the structural differences between the US and EU contributed to a productivity gap. As highlighted by Van Ark et al. (2008) [15], this gap is rooted in persistent challenges related to labor market rigidity, underinvestment in the digital infrastructure, and the slower diffusion of innovation across European markets. Recent studies by Soyres et al. (2024) [16] and Cortes et al. (2022) [17] highlighted the US’s ability to deploy aggressive monetary interventions that ease economic shocks with the help of the Federal Reserve. Dedola et al. (2020) [18] state that the usage of quantitative easing (QE) announcements by the Federal Reserve resulted in persistent exchange rate depreciation and monetary easing via the signaling channel, thereby supporting U.S. competitiveness.

This could be observed in the recovery after the COVID-19 pandemic. The EU, by contrast, faces institutional constraints on fiscal coordination and monetary flexibility, making it more vulnerable to prolonged economic stagnation and inflationary pressures. This vulnerability is amplified further by Europe’s historic reliance on external energy imports, particularly from Russia. The war in Ukraine brought these risks into sharp focus, leading to inflation surges and forcing countries to adopt emergency energy procurement strategies. Many of these have been less efficient and more carbon intensive [19]. In this environment, smart energy systems and digitalized building infrastructure have emerged, not only as tools for reducing emissions, but also as pragmatic responses to energy insecurity and economic fragility. Against this backdrop, the SRI framework offers significant potential as a stabilizing instrument capable of promoting operational efficiency, increasing cost predictability, and accelerating the integration of demand-side flexibility in buildings.

In such member states as Slovenia, where structural economic limitations constrain the ability to respond quickly to macroeconomic shocks, the SRI implementation can act as a resilience-enhancing strategy. By reducing energy dependence, lowering operational expenditures, and aligning buildings with long-term EU policy goals (such as those set out in the Renovation Wave [19] and the Green Deal [20]), the SRI adoption supports both economic competitiveness and climate neutrality. Framing the SRI in this way underscores its relevance not only as a technical metric, but also as part of a broader economic strategy for sustainable growth, innovation diffusion, and market convergence across the EU.

As large buildings consume a lot of energy, their potential for savings is also the greatest. For example, commercial buildings account for 11% and 4% of Europe and China’s total final energy demand, respectively [20]. Therefore, they are expected to benefit most from the SRI scheme [5]. There is a clear need to accelerate building renovation investments and leverage smart, energy-efficient technologies across Europe’s building sector [21]. Large buildings use building management systems (BMS), building automation systems (BAS), energy management systems (EMS), or building energy management systems (BEMS) [22]. Together, they have the task of providing energy-efficient, economical, and safe operation of building services [22]. The functions of both systems and subsystems are standardized in the European Standard EN 15232 [23]. Based on these theoretical foundations, this paper aims to use the SRI methodology and evaluate it in a case study building—a shopping center in Slovenia. The European wholesale and retail sector is a big marketplace of Europe, contributing around 11% of the EU’s GDP. Therefore, the sustainability of the retail sector may contribute significantly to reaching the EU’s long-term environmental and energy goals. Within the retail sector, shopping malls are particularly interesting due to their structural complexity and multi-stakeholder decisional process, the high potential of energy savings and carbon emissions reduction, as well as their importance and influence on shopping tendencies and lifestyle. Western Europe offers opportunities for energy-efficient refurbishment and redevelopment. The shopping center renovation rate is 4.4%—very high compared with the renovation rate of 1–1.5% in the residential sector [24].

According to the current literature, different authors have been dealing with smartness evaluation in buildings and different investments in these systems. A few papers have emerged recently since the SRI was presented.

Janhunen et al. [25] conducted a study that examined a 100,000 m² shopping center in Finland before and after major retrofitting. These changes included upgrades to the photovoltaic power plant, battery storage, active LED lighting, electric vehicle chargers, and an advanced control management system. The research assessed the economic viability of investing in smart building systems integrating renewable energy and energy storage. The €6 million investment yielded an over 10% return and increased the property value by more than €10 million, supported by a €2 million government subsidy. While financial profitability is crucial, branding and environmental performance influence investment decisions. The study used semi-structured interviews and real investment data.

Another study by Janhunen et al. [26] evaluated the financial benefits of demand-side management (DSM) in ground-source heat pump (GSHP) systems for real estate owners in the Helsinki Metropolitan Area. The study found that while DSM can reduce peak electricity consumption, it generates only €0.03/m²/year in cash flows, equating to a mere 1% saving in energy costs, with carbon emissions reduced by just 0.02%. The financial incentives for property owners to engage in reserve markets are weak. The research suggests that alternative monetary incentives and advanced smart electricity control strategies are needed to enhance value creation and encourage participation in DSM.

Bisello et al. [27] examined how increased energy efficiency in new buildings impacts real estate prices in Bolzano, Italy. They focused on the market’s response to higher energy certificate ratings, noting that all new buildings must meet energy class A standards. Using a hedonic price model, they found that energy certificates with higher scores influence property prices positively. The authors highlighted that investments in energy efficiency boost real estate values, regardless of energy savings, suggesting a “spillover effect” that influences both market prices and legislative processes.

Apostolopoulos et al. [28] evaluated the SRI for residential buildings in five European countries, focusing on single-family and multi-family houses. Their study assessed the potential for retrofitting to meet the Nearly Zero-Energy Buildings (NZEB) standards. A three-step assessment showed that retrofitting improves the SRI scores significantly, often exceeding 50%. Newer buildings achieved higher SRI scores at lower costs than older ones, with average retrofitting costs around €103 per square meter for single-family houses. Key areas for improvement included heating systems and vehicle-to-grid (V2G) capabilities.

Canale et al. [29] examined the SRI for residential buildings in Italy, assessing its effectiveness in adapting to occupant needs and the energy network. The study evaluated eight smart building typologies across three scenarios: the current state, a simple energy retrofit, and a smart energy retrofit. Their findings revealed the underutilization of smart systems, particularly in smaller homes. The authors recommend refining the SRI methodology to reflect energy performance better and conducting statistical analyses of the existing automation systems.

Galal et al. [30] discussed the need for a tailored approach to assess the smart readiness of buildings and cities in Egypt, highlighting such challenges as infrastructure deficits and high initial costs. They argued that international smart city frameworks do not address Egypt’s unique context adequately, necessitating a specific measurement tool. The research focused on the energy sector, and proposed a checklist of 21 indicators—14 for buildings and 7 for cities—to evaluate their smart readiness based on energy consumption, occupant comfort, and the ICT infrastructure. This checklist aims to help decision-makers identify necessary interventions for smarter buildings and cities.

Plienaitis et al. [31] assessed the SRI for educational facilities, focusing on a case study at Kaunas University of Technology. The SRI score for the building was calculated to be 26%, with strengths in energy savings (54%) and information availability (45%) but a weakness in grid flexibility (5%). The study used the IDA ICE simulation tool to evaluate energy performance and identify potential savings through system upgrades. While the SRI is valuable for evaluating building smartness, the research highlights gaps in its application for non-residential buildings and the need for adaptation to different building types.

Becchio et al. [7] investigated the financial implications of implementing smart technologies in buildings using the SRI. While initial investments in smart technologies may seem high, they often result in significant long-term savings and improved energy efficiency. The SRI assesses a building’s readiness for these technologies, which can lower utility bills and enhance occupant comfort through better energy management. Additionally, smart-ready buildings can attract higher rental prices and increase property value, with potential government incentives making upgrades more affordable. The study highlighted that investing in smart building technologies is a financially sound decision with substantial long-term benefits.

Fokaides et al. [32] conducted a study that evaluated a case study building, revealing a total SRI score of 52%, indicating a discrepancy between its smartness and its energy performance class (D). The research emphasized improved methodologies and minimum requirements for the SRIs in new and refurbished buildings. It also called for monetizing smartness in building systems to enhance energy savings.

Autio et al. [33] studied how popular energy efficiency improvement practices align with the EU’s smart energy transition targets in real estate using the smart readiness indicator (SRI) framework. An analysis of 49 inspection reports from Finland showed that the greatest increase in smart readiness occurs in heating, controlled ventilation, and monitoring/control categories, while other areas such as cooling and lighting are often neglected. The study highlighted a predominant focus on economic benefits, sidelining occupant needs, and adaptability to energy grids.

Considering the presented studies, we aimed to provide an in-depth study of the SRI evaluation for our case study building which has non-ideal market conditions. We wanted to evaluate the SRI in a real estate market without smart grids, “predictive” functions, and demand-side management (DSM) functions. The paper explores and determines the real financial costs of the upgrades that would result in the maximum possible SRI score. This pragmatic approach demonstrates the feasibility of building automation in constrained environments, offering insights for real estate stakeholders facing similar challenges. Verbeke [34] defined the maximum SRI scores as the maximum obtainable scores. To the best of the authors’ knowledge, there is no paper to date that explores the link between the SRI scores and their feasibility. Therefore, we proposed the term and approach considering the maximum feasible SRI score. The paper is structured as follows: Section 2 provides the methodology of the study. The results for the different scenarios are presented in Section 3. The financial analysis is elaborated in Section 3.5.1, Section 3.5.2, Section 3.5.3, Section 3.5.4, Section 3.5.5, Section 3.5.6, Section 3.5.7 and Section 3.5.8. The following Section 4, discusses the results, and Section 5 presents the main conclusions.

2. Methods

The official Excel calculation tool, version 4.5, version B, was used to calculate the SRI scores. Some authors refer to this method as the “Clipboard method” [35]. The tool was released after the final report on the technical support to develop a smart readiness indicator for buildings by Stijn Verbeke, Dorien Aerts, Glenn Reynders, Yixiao Ma, and Paul Waide [36]. For the financial calculation, we used the mathematical method, and the descriptive method was implemented for describing the facts, examining and describing the results. All the terminology and definitions utilized in this research conform to the official smart readiness indicator (SRI) methodology as delineated by the European Commission, in conjunction with the SRI Support Team, which includes VITO (Belgium), Waide Strategic Efficiency (Ireland), R2M Solution (France), and Luxembourg Institute of Science and Technology (LIST) [34].

2.1. Financial and Technical Evaluation Assumptions

To ensure a comprehensive and realistic financial and technical analysis of the upgrades proposed in this study, we established a set of assumptions related to energy prices, maintenance costs, and equipment lifespan. These assumptions are aligned with the EU standards and reflect the regional context of the case study building located in Slovenia. To estimate the cost of the technical upgrades required for each scenario, market-based pricing information was collected from technical solution providers operating in the region.

2.2. Energy Price Assumptions

The financial analysis of energy savings is based on the projected electricity and heating prices, reflecting both the current and anticipated future trends. The primary data source for electricity prices is the Eurostat database [37]. The prices for non-household consumers in Slovenia in 2024 were used. The maintenance cost and equipment lifespan assumptions are presented in

Table 1. Basic assumptions of the study.

Component	Energy Prices (EUR/kWh)	Maintenance Cost (%/Year)	Lifespan (Years)	Legislation Framework
Electricity (general)	0.21	N/A	N/A	N/A
District heating	0.08	N/A	N/A	N/A
BMS	N/A	1.5%	15	Hardware and software lifecycle assessments
PV system	N/A	1%	25	EU Renewable Energy Directive (2018/2001)
HVAC systems	N/A	2%	20	Manufacturers’ guidelines
EV charging infrastructure	N/A	1.2%	10	Manufacturers’ warranties and field data from similar installations in the EU

.

2.3. Research Design

The results of determining the SRI indicator are as follows:

• the total SRI value of the building (expressed as a percentage),
• domain scores (expressed as a percentage),
• impact scores (expressed as a percentage).

The domain scores include the evaluation of heating, domestic hot water, cooling, ventilation, lighting, dynamic building envelope, electricity, EV charging, and monitoring and control. The impact factors include energy savings, flexibility for energy grid and energy storage, comfort, convenience, well-being and health of users, maintenance and anticipation of errors, and information for occupants [36]. For the case study building, we wanted to explore the three scenarios stated in

Table 2. Three different scenarios.

Scenario A	Scenario B	Scenario C
Baseline scenario (before interventions)	After interventions (new AHUs, new PV plant, upgrade of BMS, etc.)	Examination of needed interventions to reach the maximum obtainable scores according to the SRI methodology

. The flowchart of the study is presented in Figure 1.

2.4. Case Study Building

The case study building was examined in the summer/autumn of 2023 as part of the research work. An author of this paper participated in the project as a building management system (BMS) provider representative. The case study building is a large, two-story shopping center. Compared to comparable facilities in the wider real estate market, the building has a relatively large number of technical systems installed, run by a relatively advanced building management system. The facility is located in Slovenia. According to the SRI methodology, the building is classified as a non-residential building with the usage classified as “other.” The main heat source is district heating.

Five cold-water chillers are used for cooling. The center is ventilated with 21 air handling units (AHUs), mainly on the facility’s roof. Individual shops in the shopping center are connected to the duct network of the ventilation system. Each shop can control the airflow using electronic flow regulators (VAV systems). The temperature in the shops is regulated with local duct water heaters and coolers. Due to the architectural specifics of the building, most of the shops do not have direct access to windows; therefore, an advanced shading system is not necessary. The facility has a modern building management system (BMS) that controls the heating, cooling, and ventilation (HVAC) systems. The system visualizes vital data and offers schedulers, alarms, etc. The data about energy consumption (heat energy, electrical energy) are stored in the energy monitoring system (EMS). The main data pertaining to the facility are listed in

Table 3. General information about the case study building.

Category	Description
Building type	Shopping center
Total area/area for shops	156,000 m2/40,000 m2
Year of construction	2000
Number of shops Number of parking spots	120 2600
Heating	District heating
Domestic hot water heating	No central hot water heating
Ventilation system	21 air handling units after the upgrade. The devices have CO2 detection where necessary, recuperation (plate heat recovery or a recovery wheel)
Cooling	Central cooling, 5 chillers
Dynamic building envelope	Not available
Electricity production	Yes, after the upgrade, a photovoltaic power plant with 842 kW of power. Yearly production is planned for around 978 MWh.
Charging for electric vehicles	Yes, 5 chargers before the upgrade; after the upgrade, 15 chargers connected to an online portal (gremonaelektriko.si)
Central management	Yes, a building management system (BMS) with an energy monitoring system (EMS)

.

3. Results

This section presents the results of the triage procedure of the baseline scenario A (see Section 3.1), the triage procedure of scenario B (see Section 3.2), the interventions needed to achieve the maximum score, Scenario C (see Section 3.3), the achieved SRI scores of Scenario C (see Section 3.4). The financial analysis of all the three scenarios is presented in Section 3.5.

3.1. Triage Procedure for the Baseline Scenario—Scenario A

The total SRI score is 28%, which is an average result. Considering the building’s automation equipment, the total score could be higher.

3.1.1. Domain Scores

The achieved domain scores reflect the overall situation at the building before the interventions. An average score was received for heating (41.8%), cooling (39.7%), ventilation (31.4%), and lighting (29.3%). The score for heating is quite high, although the facility uses district heating and no renewable heat source. The score for domestic hot water is 0.0%, since the building does not have the function of central domestic hot water preparation. The scores for heating and cooling are comparable. For lighting, the correct service level in the Excel calculation tool is hard to consider, because different shops installed different equipment that controls the lighting system. Therefore, an average, level 1 service was selected that describes the base level for the entire center (shops and hallways). As noted, the building does not have a dynamic building envelope (in other words, shading); therefore, it was excluded from the calculation. The score for electricity (14.7%) is low. The lowest score was for the domain of electric vehicle charging: −27.8%. A battery storage system would raise both scores. The monitoring and control domain received a score of 37.4%, although it is modern and updated to the latest version.

3.1.2. Impact Factors

The highest impact factor scores were energy efficiency (54.3%) and comfort (55.4%). Energy flexibility and storage received the lowest score of 1.7%. The assessment is justified because the facility does not use technical systems that would contribute to energy flexibility and energy storage. The next four impact factors are comparable: convenience, 34.2%; health, well-being, and accessibility, 35.9%; maintenance and fault prediction, 33.1%; and information for occupants, 28.6%. These four have quite low scores but are suitable for the condition of the building before the updates.

Table 4. Results of the domain and impact factor scores of the case study building.

Domain Scores	Heating	DHW	Cooling	Ventilation	Lighting	Dynamic Building Envelope	Electricity	Electric Vehicle Charging	Monitoring and Control
	41.8%	0.0%	39.7%	31.4%	29.3%	0.0%	14.7%	−27.8%	21.1%
Impact factor scores	Energy efficiency		Energy flexibility and storage	Comfort	Convenience	Health, well-being, and accessibility	Maintenance and prediction		Information for occupants
	54.3%		1.7%	55.4%	34.2%	35.9%	33.1%		28.6%

and Figure 2, Figure 3 and Figure 4 present the triage procedure results for Scenario A.

3.2. Triage Procedure—Scenario B

The case study description mentioned briefly that the building systems had several updates in the previous three years. All lighting inside and outside was replaced with modern LED technology. The glassing was replaced on the roof above the hallways. An additional 10 stations were added to the 5 existing charging stations for electric vehicles. The total number of installed charging stations was 15. On the roof of the building, a photovoltaic power plant was installed with the capacity of 842 kW. All the air handling units were replaced with modern ones [38]. Accordingly, an energy monitoring system was installed, and the control and building management system hardware was upgraded. The comparison between the SRI scores before and after interventions is presented in Figure 5.

The total SRI score rose from 28% to 38%. Considering the updates, the increase in the general SRI score is satisfactory.

3.2.1. Domain Scores

The domain score for heating remained the same, 41.8%. It seems correct because there were no direct changes to the heating system. The domestic hot water domain also remained the same, 0.0%, as the building does not have this function. Although the chillers for the cooling water preparation were changed, the core for the cooling domain remained the same, 39.7%. This confirms that the methodology is, in general, acceptable, and that it only takes into consideration the automation equipment of the building. The ventilation domain saw a significant leap, from 31.4% to 62.9%, which was expected, because the main part of the hardware upgrade was focused on the exchange of the air handling units. For the lighting domain, the lights were replaced with LED technology, and the domain score remained the same, 29.3%. The control system for the lighting also remained the same. Therefore, the score did not change. The domain score for electricity climbed from 14.7% to 24.2% with the help of the photovoltaic power plant on the roof of the building. However, the domain score was not higher because the locally generated electricity is fed into the electric network instead of into batteries.

The next domain is electric vehicle charging, which increased from −27.8% to 8.3%. The shopping center has 2600 parking spots. Even though the number of charging stations for electric vehicles increased from 5 to 15, this did not change the functionality from level 2 to level 3. This functionality level predicts that 0–9% of all the parking spots can charge an electric vehicle. In our case, this would mean that the facility would have from 0 to 234 chargers for electric vehicles. The reason that changed the score from −27.8% to 8.3% is that these chargers are now connected to a smart platform (the Slovenian EV platform http://gremonaelektriko.si, accessed on 17 May 2025) that reports information on EV charging status, including automatic identification and authorization of the driver, to the charging station (complies with ISO 15118) and the occupants. ISO 15118 defines the communication between electric cars and the charging infrastructure. It defines a common language and rules ensuring energy transfer between vehicles and charging stations. It also plays a crucial role in promoting the widespread adoption of electric vehicles, as it addresses concerns regarding compatibility and ease of use [38]. The standard delivers important features for smart charging (connection to the web, information for users over applications, etc.) [39]. The domain score for monitoring and control increased from 21.1% to 35.9%, as the BMS was upgraded, and an energy monitoring system was added.

3.2.2. Impact Factors

The first Impact Factor score, energy efficiency, increased from 54.3% to 59.7%, presenting a slight change. Because no changes were made to the heating system, the change in the impact score comes mainly from the more efficient control of the air handling units. The actual higher recuperation rate and, therefore, more efficient air handling units are not considered in this change of the impact score for energy efficiency. The change comes solely from the way of controlling the AHUs. The impact score for energy flexibility and storage increased slightly from 1.7% to 2.6%. Comfort, as the next impact score, increased from 55.4% to 64.7%, which is reasonable. Better air quality leads to better overall comfort in the building. Convenience, as the next impact score, increased from 34.2% to 45.6%, similar to health, well-being, and accessibility, which increased from 35.9% to 58.7%. Maintenance and fault prediction increased from 33.1% to 51.4% due to the upgrade of the BMS. Similarly, the impact score of information for occupants increased from 28.6% to 54.6%.

3.3. Interventions Needed to Achieve the Maximum Score—Scenario C

The SRI methodology is prepared so that, for each service, 3 to 5 different levels are used for evaluating the technology installed in the building. The most technologically advanced solutions always appear at levels 4 and 5. This section analyzes the maximum level achieved for our case study building. In Appendix B the maximal achievable scores for our case study building are presented.

3.3.1. Heating

To achieve the maximum score for the first service, Heating-1a, the methodology suggests that individual room heating should be controlled by occupancy detection. Unlike our case study building, this is not the most suitable solution for a large facility (40,000 m² for shops). The 120 individual shops must stay comfortable, independent of the direct number of visitors. There are other, better ways and methods than monitoring occupancy detection. The base setpoint (for heating and cooling) is regulation with air handling units; individual needs for heating and cooling are covered with an individual variable air volume (VAV) system. Therefore, we selected the penultimate level, individual room control with communication between controllers and BACS. The next service, Heating-1b, controls the TABS (thermally activated building system). Such a system consists of water tubes that run through the concrete structure of the building. The concrete’s inertia promotes reduced heating or cooling needs [40]. Our case study building lacks such a system that cannot be retrofitted; therefore, it was exempted from the SRI calculation.

The service Heating-1c achieved the 100% score because the control of distribution fluid temperature is demand-based. The next service is Heating-1d. For the maximum score, the circulation pumps for heating should be controlled by external demand signals. This can only be possible if advanced district heating systems (4TH and 5TH generation) are available. Because they are not, we must select the penultimate level 3, which suggests that the speed of the pumps is controlled based on pump unit (internal) estimations, which is true for our case study building. In our case study, the thermal energy storage from the service Heating-1f is controlled by a schedule. For the maximum SRI score, thermal energy storage should be managed by flexible control through grid signals (demand side management, or DSM). DSM is a method of limiting peak energy consumption. A dynamic operation by varying production or consumption levels benefits renewable electricity, the economy, and the environment [41]. As noted, the adaptation in Slovenia is in the early stages. The National Energy Plan for Slovenia (NEPN), mentions briefly that DSM will be given a greater role in the future [42]. The next two services, Heating H-2b and Heating H-2d, are not used in our case study building. Therefore, they were excluded from the calculation. The service Heating H-3 evaluates if the BMS enables central or remote reporting of performance evaluation, including forecasting and/or benchmarking (also including predictive management and fault detection). Such features are described in the current literature but are not yet available commercially in Slovenia. Therefore, we selected the penultimate level 3 service central or remote reporting of performance evaluation, including forecasting and/or benchmarking. This level could be achieved with some upgrades of the BMS and the energy monitoring system. The last service in this Section for heating is Heating H-4, flexibility and grid interaction. The methodology forecasts that the heating system should be optimized based on local predictions and grid signals (through predictive control). Levels 3 and 4 suggest the usage of DSM and grid signals (e.g., through model predictive control). As highlighted, these solutions are not available commercially in Slovenia, therefore, they cannot be reached. Consequently, we selected level 2, self-learning optimal control of heating system, which could be achieved with an upgrade of the control system.

3.3.2. Domestic Hot Water

The facility does not have a central system for hot water preparation; therefore, this category was excluded from the calculation.

3.3.3. Cooling

In achieving the maximum score for the service Cooling C-1a, the cooling should be controlled depending on the presence control, such as the service Heating 1-a. For the same reasons that are described in Heating, we also selected individual room control with communication between controllers and BACS as the maximum achievable level. Because the building does not have a TABS, the category Cooling C-1b was also excluded from the calculation. The service Cooling C-1c, similar to heating, achieved the maximum level because the distribution network’s chilled water temperature is controlled based on demand. The service Cooling C-1d describes the control of distribution pumps. The pumps are controlled variably, based on the pump’s internal estimations. Similar to heating, there are no smart networks that would control the pumps based on external signals. The next service, Cooling C-1f, achieved the maximum level because an interlock between heating and cooling is in place. The next service is Cooling C-1g. The maximum service level could not be reached due to DSM’s absence. Therefore, we selected thermal energy storage based on load predictions, similar to heating. The service Cooling C-2a evaluates the control of the chillers. The maximum score predicts signals from the smart grid that are absent. Two chillers have already reached the penultimate level 2. The remaining three chillers are on/off controlled; therefore, we assume a replacement with modern chillers, so that all of them will reach level 2. The next service, Cooling C-2b, evaluates the sequencing of the different chillers. Usually, more than one chiller is required to meet the load of the entire building. Each chiller has different efficiencies in full-load and part-load conditions. As the load on the chillers increases, operating more than one chiller is necessary to meet the demand. It is vital to sequence the operation of the chillers based on the load, so that they operate most of the time at their peak efficiencies. For example, running two chillers at their peak load efficiency could be more efficient than running one chiller at full capacity or three at low capacity, where their efficiency is decreased considerably [20].

The top level predicts sequencing based on grid signals. This is unattainable, due to the lack of smart grids. Therefore, we will aim at the penultimate level; here, the sequencing is based on load prediction, the COP of devices, and the predicted required power. Next, the service Cooling C-3 evaluates if the BMS enables central or remote reporting of performance evaluation, including forecasting and/or benchmarking (also including predictive management and fault detection). Similar to heating, these functions are described in the current literature but are not yet available commercially in Slovenia. Therefore, we are selecting the penultimate, level 3 service, central or remote reporting of performance evaluation, including forecasting and/or benchmarking. This level could be achieved with some BMS and energy monitoring system upgrades. The last service in this section for cooling is Cooling C-4, flexibility and grid interaction. The methodology forecasts that the cooling system should be optimized based on local predictions and grid signals (through predictive control). Levels 3 and 4 suggest using DSM and grid signals (e.g., through model predictive control). As stated, these solutions are unavailable in Slovenia. Consequently, we selected level 2, self-learning optimal control of cooling system, which could be achieved with an upgrade of the control system.

3.3.4. Air Flow

The service Air flow V-1a describes how the supply airflow is controlled at the room level. The maximum level 4 predicts that the local demand is based on an air quality sensor with local flow from/to the zone regulated by the dampers. The facility has an installed VAV that was modernized, and with which we did not interfere due to its extensiveness. Therefore, we selected level 3, central demand control based on air quality sensors (CO₂, VOC, humidity, etc.) installed at the AHU level during the modernization. The next service, Air flow V-1c, describes the conditions in the distribution system. The maximum level 4 suggests automatic flow or pressure control with a pressure reset: load-dependent supplies of air flow for the demand of all the connected rooms (for the variable air volume systems). This level cannot be achieved without structural interventions and investments in our case study building. Therefore, the feasible level would be level 3, automatic flow or pressure control without a pressure reset: load-dependent supplies of air flow for the demand of all the connected rooms. The next service, Air flow V-2c, describes the heat recovery control: prevention of overheating. The maximum level 2 suggests modulating or bypassing heat recovery based on multiple room temperature sensors or predictive control. This level could be achieved with the additional hardware and software upgrades considered in the financial calculation. The next service, Air flow V-2d, describes the supply air temperature control at the air handling unit level. The maximum level 3 predicts a variable setpoint with load-dependent compensation. A control loop enables control of the supply air temperature. The setpoint is defined as a function of the loads in the room. This function would need intensive interventions; therefore, the current Level 2 remains in place: variable setpoint with outdoor temperature compensation. Next is the service Air flow V-3, which describes the control for free cooling. The maximum level 3 suggests that free cooling should be controlled based on the temperature and humidity enthalpy. This function is more common in industrial processes, not building applications. Special sensors would be needed at the level of the air handling units, additional reprogramming of the control units, etc. Consequently, level 2 remains in place: free cooling, air flows modulated during all periods to minimize mechanical cooling. The last service in this section is Air flow V-6, reporting information regarding indoor air quality. The maximum level 3 suggests real-time monitoring and historical information of indoor air quality to occupants + warning on maintenance needs or occupant actions (e.g., window opening). As stated, no windows can be opened manually; the building has 21 air handling units that supply fresh air to all building parts. Therefore, we have kept level 1, which has already been achieved. Air quality sensors are installed on the exhaust side of the air handling units. The data from them are collected in the building management system.

3.3.5. Lighting

The service Lighting, L-1a evaluates how occupancy influences the indoor lighting. The maximum level 3 predicts automatic detection (manual on/dimmed or auto off). This level is technically not feasible due to the nature of the building. The heating/cooling comfort and the lighting conditions in the individual shops have maximum priority as mentioned for Heating. The lighting in common areas, such as restrooms, technical rooms, etc., is controlled based on presence detection. However, most of the lighting is controlled by a dedicated BMS. We considered setting up an additional sweeping extinction signal in the financial calculation. We left the current level 1 as the maximum feasible in our case study building. The second service, Lighting L-2, examines the possibility of controlling the artificial lighting power based on daylight levels. The maximum level 4 predicts an automatic dimming system that includes scene-based light control (during time intervals, a dynamic and adapted lighting scene can be set, for example, in terms of illuminance level, different correlated color temperature (CCT), and the possibility to change the light distribution within the space according to design, human needs, visual tasks. The control in this case is advanced and sophisticated but would be technically challenging to implement in our case study building. Therefore, we did not select it as the maximum level. Instead, we selected the actual level 3 as the maximum feasible level. This cannot be selected, although all the lighting in the center was changed to LED technology as per the SRI methodology.

3.3.6. Window Control

As noted previously, the building does not have windows that can be opened manually. Therefore, this category was excluded from the calculation.

3.3.7. Electricity

The service Electricity E-2 describes how the local energy generation is reported. In our case, this is a PV plant on the center’s roof. To achieve the maximum level 4, performance evaluation should include forecasting, benchmarking, predictive management, and fault detection. Currently, level 2 has been achieved, where the control interface of the PV provides actual and historical data. Level 4 could be achieved with software upgrades included in the financial calculation. Next, Electricity E-3 evaluates the possibility of storing the locally generated electricity from the PV plant. The maximum level 4 predicts an on-site storage of electricity (e.g., electric battery or thermal storage) with a controller optimizing the use of locally generated electricity and the possibility to feed it back into the grid. Currently, no storage capabilities are installed on site. Therefore, the fastest way to raise the level of smartness would be to install some storage in the form of battery power banks. This way, we would reach level 1, on-site storage of electricity (e.g., an electric battery). All the other levels use connections to smart grids and are unattainable for our case study in the short term. The next service is Electricity E-4, which describes optimizing self-consumption of locally generated electricity. The maximum level 3 predicts automated management of local electricity consumption, based on the current and predicted energy need and renewable energy availability. This level is out of reach as long as smart grids are unavailable. Therefore, in the financial calculation, we considered level 2, automated management of local electricity consumption based on current renewable energy availability. The service Electricity E-5 evaluates the level of control of combined heat and power plant production (CHP). This is not available in our case study; therefore, this service was excluded from the SRI calculation. The service Electricity E-8 describes how the building supports microgrid operation modes. The maximum service level would enable automated management of (building-level) electricity consumption and supply, with the potential to continue limited off-grid operation (island mode). In addition, the other two levels, 1 and 2, rely on the functionality of smart grids. Therefore, the real feasible level in this case is level 0. Next is the service Electricity E-11, which describes the reporting information regarding energy storage. The maximum level 4 predicts that the building has the ability of performance evaluation, including forecasting and/or benchmarking; also including predictive management and fault detection. This level would be challenging and financially demanding to achieve; therefore, level 3, performance evaluation, including forecasting and/or benchmarking, is feasible. The last service, Electricity E-12, describes the possibility of reporting information regarding electricity consumption. The maximum level 4 predicts real-time feedback or benchmarking on the appliance level with automated personalized recommendations. The automated personalized recommendations are not yet available commercially in Slovenia. Therefore, we selected level 3, because the building already uses several power meters that monitor the consumption of large consumers (AHUs). The collected data are stored in the energy monitoring system.

3.3.8. EV Charging

The service EV charging EV-15 evaluates the EV charging capacity. As stated, the number of EV chargers increased from 5 to 15; however, this did not change the service level. The maximum level 4 predicts that more than 50% of all parking spots are equipped with charging stations. The achievability of this suggestion is discussed in Section 4.3. We selected level 2, the actual level, as the maximum achievable level. Next, the service EV charging EV-16 evaluates the grid-balancing capabilities. The maximum level 2 suggests two-way controlled charging (e.g., including the desired departure time and grid signals for optimization). The EV infrastructure is under construction; the viable selection is one-way controlled charging (e.g., including the desired departure time and grid signals for optimization), which is also included in the financial calculation. The service EV charging EV-17 evaluates the EV charging information and connectivity. In our case, the EV charging stations are connected to the online platform, where users can check the availability of charging stations, the prices, the power output, etc. The newly installed stations support ISO 15118 [43]. The maximum level of this service was reached.

3.3.9. Monitoring and Control

The first service, Monitoring and control MC-3, describes how the runtime of the HVAC systems is managed. The maximum level 3 suggests that the heating and cooling plant are on/off-controlled based on the predictive control of grid signals. This level cannot be reached due to the lack of smart grids. Therefore, the penultimate level 2, heating and cooling plant on/off-controlled based on building loads, can be reached with software upgrades. The next service is Monitoring and control MC-4, which evaluates the ability to detect faults in technical building systems and provides support to diagnose these faults. A central indication of detected faults and alarms for all relevant TBS, including diagnosing functions, should be established to achieve the maximum level 3. In our case, the diagnosis part depends on subordinate systems and subsystems. Therefore, selecting level 2, which does not predict the diagnosis functions, would be more viable. Next is Monitoring and control MC-9, which describes occupancy detection and how it affects the connected services. Occupancy detection was already mentioned in the sections for heating and cooling. The maximum level 2 suggests centralized occupant detection feeds into several technical building systems such as lighting and heating. This level can be achieved and used at least for lighting. The next service is Monitoring and control MC-13, which evaluates the central reporting of TBS performance and energy use. Our case study building reaches category level 2; there is a central or remote reporting of real-time energy use per energy carrier, combining TBS or at least 2 domains in one interface. To achieve the maximum level 3, all the main domains should be seen in one interface. This level could be achieved with an energy monitoring system upgrade and additional subsystem integrations. The next service, Monitoring and control MC-25, evaluates the possibilities for smart grid integration. As already highlighted, the functionalities of the smart grid are not available. Therefore, the building gets the lowest level 1 score, none. Next, Monitoring and control MC-28 evaluates the possibilities of reporting information regarding demand-side management performance and operation. Similar to the topic of smart grids, demand-side management has also not been implemented yet. Therefore, our selection is Level 0, none, the lowest level. Next, the service Monitoring and control MC-29 evaluates the possibility of overriding the DSM control. As in service MC-28, we can also select the lowest level 0, none, due to the lack of DSM. The last service is Monitoring and control MC-30, which evaluates if there is one single platform that allows automated control and coordination between technical building systems, that serves for the optimization of energy flows based on occupancy, weather, and grid signals. Due to the lack of smart grid functions in our case study building, we could reach the maximum functionality level 2, one single platform that allows automated control and coordination between technical building systems.

3.4. Achieved SRI Scores—Scenario C

Above, we highlighted that many of the services suggested in the SRI calculation methodology could not be implemented due to the lack of smart grid infrastructure, the absence of predictive functions in commercially available BMS, and other factors. However, if all the possible measures were implemented, the SRI scores would be as represented in

Table 5. Results of the domain and impact factor score for the case study building—the maximum scores that can be achieved.

Domain Scores	Heating	DHW	Cooling	Ventilation	Lighting	Dynamic Building envelope	Electricity	Electric Vehicle Charging	Monitoring and Control
	64.6%	0.0%	66.7%	72.4%	64.0%	0.0%	66.0%	63.9%	50.4%
Impact factor scores	Energy efficiency	Energy flexibility and storage		Comfort	Convenience	Health, well-being, and accessibility	Maintenance and prediction		Information for occupants
	84.0%	31.9%		88.5%	68.4%	77.2%	63.0%		75.5%

. The comparison between the scores for all three scenarios is also presented in Figure 6. The breakdown of Scenarios A, B, C and the SRI scores are presented in

Table 6. Breakdown of Scenarios A, B, C and the SRI scores.

	Scenario A	Scenario B	Scenario C
Domains
Heating	41.8%	41.8%	64.6%
Domestic hot water	0%	0%	0%
Cooling	39.7%	39.7%	66.7%
Ventilation	31.4%	62.9%	72.4%
Lighting	29.3%	29.3%	64.0%
Dynamic building envelope	0%	0%	0%
Electricity	14.7%	24.2%	66.0%
EV charging	−27.8%	8.3%	63.9%
Monitoring and control	21.1%	35.9%	50.4%
Impact factors
Energy efficiency	54.3%	59.7%	84.0%
Energy flexibility and storage	1.7%	2.6%	31.9%
Comfort	55.4%	64.7%	88.8%
Convenience	34.2%	45.6%	68.4%
Health	35.9%	58.7%	77.2%
Maintenance	33.1	51.4	63.0
Information for occupants	28.6	54.4	75.5
Total SRI score	28.0	38.0	60.9

.

3.5. Financial Analysis

We divided the interventions into three Scenarios: A, B, and C. Scenario A represents the building before retrofitting actions. The total SRI score of this scenario is 28.0%. After the retrofitting, the building achieved a total SRI score of 38.0%. The third scenario is C, where we tried to reach the maximum obtainable scores. The details are presented in

Table 7. Financial breakdown of Scenarios A, B, and C.

Scenario A	Scenario B	Scenario C
Total SRI score: 28.0%	Total SRI score: 38.0%	Maximum feasible SRI score: 60.9%
Baseline scenario	An investment of 6.6 million EUR was needed to reach Scenario B. Amount of the subsidy—168,428 EUR for the PV plant from the Republic of Slovenia and the European Union from the Cohesion Fund	Additional 1.38 million EUR would be needed to reach Scenario C
N/A	The ratio between the investment and the usable area of 40,000 m2: 165.79 €/m2	The ratio between the investment and the usable area of 40,000 m2: 200.26 €/m2

.

3.5.1. Financial Breakdown for Scenario B

The retrofit according to scenario B included the replacement of 21 air handling units (total with installation costs, handling costs, and additional costs for electrical and mechanical work), installation of 10 additional EV charging stations, replacement of the glassing on cupolas (roofing), the upgrades of the BMS and the energy monitoring system. The costs for all this work were 4.5 million EUR. More than 2000 photovoltaic panels with an 842 kWh power output (estimated yearly electricity production of 978 MWh) were installed on the center’s roof. The total costs of the power plant were 1.2 million EUR. Installing a photovoltaic power plant was entitled to a state subsidy of 168,428 EUR The total investment costs in scenario B (including all additional and indirect costs in 2024) were 6.6 million EUR (investments minus subsidy).

3.5.2. ROI and Payback Period for Scenario B

To evaluate the financial viability of the proposed smart readiness upgrades, this section presents a return on investment (ROI) and payback period analysis. The objective is to quantify the economic performance of Scenario B comprising the newly installed equipment. ROI and payback period are widely used metrics that provide insights into the profitability and risk associated with energy-related investments. The input data is presented in

Table 8. Input data and estimations for the IRR, ROI and payback period calculation.

Input Data
Shopping area	40,000	m2
Average electricity consumption (B)	150	kWh/m2
Baseline heating consumption of the center (district heating)	1,500,000	kWh/year
Electricity cost per kWh	0.21	€/kWh (Eurostat, for year 2024, including taxes and all fees) [44]
Electricity feed-in tariff	0.29	€/kWh (Eurostat, for year 2024, including taxes and all fees) [44]
District heating costs per kWh (E)	0.8	€/kWh (Eurostat, for year 2024, including taxes and all fees) [44]
Power of the PV plant	842	kW
Specific yield for the location in Slovenia (Y)	1150	kWh/kWp/year

.

The detailed calculation in presented in Appendix A.

Payback period is calculated as shown in Equation (1):

$$\mathrm{P}\mathrm{a}\mathrm{y}\mathrm{b}\mathrm{a}\mathrm{c}\mathrm{k} \mathrm{p}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{o}\mathrm{d}={\displaystyle \frac{\mathrm{I}\mathrm{n}\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{c}\mathrm{o}\mathrm{s}\mathrm{t}\mathrm{s}}{\mathrm{A}\mathrm{n}\mathrm{n}\mathrm{u}\mathrm{a}\mathrm{l} \mathrm{n}\mathrm{e}\mathrm{t} \mathrm{c}\mathrm{a}\mathrm{s}\mathrm{h} \mathrm{f}\mathrm{l}\mathrm{o}\mathrm{w}}}$$

(1)

The ROI is calculated as shown in Equation (2):

$$\mathrm{R}\mathrm{e}\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{n} \mathrm{o}\mathrm{f} \mathrm{i}\mathrm{n}\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}={\displaystyle \frac{\mathrm{P}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{i}\mathrm{t}}{\mathrm{I}\mathrm{n}\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{c}\mathrm{o}\mathrm{s}\mathrm{t}\mathrm{s}}}\times 100$$

(2)

Table 9. IRR, ROI, and payback period for Scenario B.

Without Subsidies	IRR	ROI	Payback Period
	6%	7%	14.2 years
With subsidies
	7%	8%	12.3 years

presents the key financial indicators for Scenario B, IRR, ROI, and payback period under two investment conditions: with and without subsidies. The results show that while the IRR (6%) and the ROI (7%) remain consistent across both scenarios, the payback period is notably shorter with subsidies, improving from 14.2 years to 12.3 years. This underscores the critical role of public support mechanisms in enhancing the financial attractiveness of smart readiness investments. The IRR remains relatively modest, which could represent a vulnerability in the long run.

3.5.3. Sensitivity Analysis for Scenario B

Table 10. Results for the sensitivity Analysis for Scenario B.

Without Subsidies	IRR	ROI	Payback Period
	5%	7%	15.2 years
With subsidies
	6%	8%	13.2 years

presents the results of a sensitivity analysis simulating a moderate market shift. Specifically, the electricity purchase price increases by 15%, rising from 0.21 €/kWh to 0.24 €/kWh, while the electricity feed-in tariff decreases by 20%, falling from 0.29 €/kWh to 0.23 €/kWh. These changes reflect a scenario of reduced profitability from photovoltaic energy exports and increased operational costs, such as those that might arise from supply chain volatility or energy market instability. Despite these adjustments, the investment remains economically viable. Without subsidies, the IRR remains at 5%, the ROI improves slightly to 7%, and the payback period is 15.2 years. With subsidies, the performance improves further: the IRR increases to 6%, the ROI—to 8%, and the payback period shortens to 13.2 years. These findings indicate that even under moderately adverse market conditions, the investment scenario retains financial attractiveness, especially when supported by public subsidy schemes. This reinforces the value of incentive mechanisms in stabilizing the investment outlook for smart building upgrades.

3.5.4. What-If Analysis for Scenario B

To assess the robustness of the investment’s financial performance under changing market conditions, a sensitivity analysis was conducted. This simulation was designed to reflect a hypothetical market shock, similar in scale to the energy price volatility observed during the COVID-19 pandemic and post-crisis recovery periods. In the modeled scenario, several adverse changes were applied simultaneously:

-

The electricity purchase price increased by 24%, rising from 0.21 €/kWh to 0.26 €/kWh;

-

The electricity feed-in tariff dropped from 0.29 €/kWh to 0.22 €/kWh;

-

The equipment cost increased by 20%, simulating inflationary pressure on materials and technologies.

These changes significantly impacted both operating margins and upfront investment requirements. As a result, the internal rate of return (IRR) remained at 5%, while the return on investment (ROI) stayed at 6%, but the payback period increased to 17.6 years without subsidies and 15.6 years with subsidies, as shown in

Table 11. Results of the what-if analysis for Scenario B.

Without subsidies	IRR	ROI	Payback period
	5%	6%	17.6 years
With subsidies
	5%	6%	15.6 years

.

This example demonstrates that while financial performance is sensitive to external price shifts, the investment remains financially viable, particularly when supported by subsidy mechanisms such as those offered by Eko sklad. The scenario highlights the importance of integrating financial resilience considerations into energy planning decisions.

3.5.5. Financial Breakdown for Scenario C

For the building to reach the theoretical maximum obtainable SRI score, ca. 1.38 million EUR of additional investments would be needed. The individual proportions for different systems are represented in Figure 7.

The major investment represents the cooling domain (50%). The building uses 5 different chillers to produce cooling water. Two of them have been replaced in recent years. The calculations predict replacing the remaining three so that the cooling service C-2a, generator control for cooling, would reach level 2. This level predicts that all generators have variable control of cooling, and the production of cooling water depends on loads or demand (the newly installed devices would have the functionality of hot gas bypass and inverter frequency control).

The next service with a major investment would be electricity (26%). For this service, we predict an investment into battery storage of the electricity the photovoltaic power plant produces. For battery storage, there are not many options on the market yet. The two major providers are Tesla and N-Gen. In the calculation, we opted for the provider N-Gen with 10 battery packs, each with a power of 40 kW and a capacity of 103 kWh. The power of the PV power plant is 842 kW. With 10 power banks, we would reach a total power of 400 kW. This represents the sweet spot between the investment price, installation costs, and the required space. The number of power banks can be expanded; a maximum of 20 pieces can be stacked together. It is not realistic to expect that the building could work in an off-the-grid or island mode (as the E-8 service, “support of (micro)grids operation modes”, suggests). The battery pack would act as a temporary storage for the PV-generated electricity.

The third service is ventilation, with 9%. Here, we considered the upgrade that would enable the air flow rate without a pressure reset (V-1C). This could be achieved with additional differential pressure sensors and a control equipment upgrade. The prevention of overheating (V-2C) would require a similar control upgrade and additional temperature sensors at the room level.

The fourth upgrade is in the monitoring and control domain (7%). It is mainly a software upgrade. In the calculation, we predicted an upgrade of the monitoring and control system so that heating and cooling would be controlled based on building loads (MC-3, level 2). Next, detecting faults in technical building systems should be expanded (MC-4, level 2). Therefore, integrations of alarms and faults were foreseen for the systems not yet integrated into the main building management platform Siemens Desigo CC. A prominent role in the methodology is occupancy detection. As described in previous sections, occupancy detection is unsuitable for controlling heating in such a type of buildings. Still, emphasis could be given to controlling at least those parts of the building where this would be possible. Therefore, we foresaw new and additional presence detection features and changes in control software in our financial calculation. The methodology also predicts a central reporting and TBS performance of the energy usage system (MC-13, central reporting of TBS performance and energy use). Our calculation foresaw an upgrade and expansion of the existing energy management system. The last service in this group suggests that a single platform allows automated control and coordination between technical building systems (MC-30). In the case study building, there are currently two complementary management systems; one is the BMS (building management system) that controls the energy flows (heating, cooling, ventilation, etc.) and the other is the energy management system that collects all data from the meters (electricity meters, heat meters, etc.). Our calculation foresaw migration of energy management into the BMS.

The fifth service is heating (3%). The calculation of the investment foresaw a change in the control system so that, instead of a time-scheduled storage operation, a load prediction system is used (H-1f, level 2). Moreover, instead of a variable temperature control depending on the outdoor temperature, a variable temperature depending on the load is foreseen (H-2a). Besides the reporting functions of the current performance KPIs and historical data, the BMS should also enable performance evaluation reporting, including forecasting and benchmarking (H-3, reporting information regarding heating system performance). In the calculation, we foresaw a replacement of a scheduled operation of the heating system with a self-learning control system (H-4, flexibility and grid interaction).

The sixth group is lighting; the investments in this group represent 3% of the total investments, according to Scenario C. In the service L-2, control of the artificial lighting power based on daylight levels, the lighting system would be upgraded, so that all lighting could be dimmed when 100% of lighting power is not needed. For this, additional dimming elements would need to be installed into the electrical cabinets that control the lighting in the center.

The seventh group is EV charging, with 2%. Here, we encountered a major deficiency in the methodology applied to our case study building. The first service (EV-15, EV charging capacity, level 3) suggests that 10–50% of parking spaces should be equipped with EV charging stations. The center has 2600 parking spaces. Let us assume that there are no challenges with installing 1300 charging stations. If one charging station has 22 kW of electrical connecting power, the total power for 1300 stations would be 28.6 MW. The electrical system in the center was not designed for so many additional electrical consumers, and massive adaptations would be needed in the electrical installation.

Furthermore, most parking spaces are on several (four) floors, which were designed and constructed without considering the (additional) weight of electric vehicles. Consequently, this category opens many questions on different levels. Additionally, the financial investment in such a project (with all the additional/related costs) is difficult to evaluate. Therefore, level 2 was selected as the optimal service level for our case study building. The next service (EV-16) evaluates the EV charging grid balancing, where one-way controlled charging is evaluated (that includes the desired departure time for optimization).

The eighth and ninth services, window control and domestic hot water, are not in our case study building. Figure 8 represents the proportions of BMS costs for Scenarios B and C.

3.5.6. ROI and Payback Period for Scenario C

In Scenario C, an additional investment of €1,380,000 is proposed to further enhance the building’s smart readiness capabilities. This scenario includes advanced interventions such as predictive control and battery storage. Based on our simulations, these upgrades are expected to deliver measurable operational benefits. We are expecting a further decrease in electricity consumption (by 3%) and in heating costs (by 8%). These gains would contribute to the overall energy efficiency, reduced operational costs, and a higher smart readiness indicator (SRI) score. However, the financial payback remains more long-term in nature, emphasizing the importance of aligning such advanced investments with broader policy incentives or long-range sustainability goals. The IRR, ROI and payback period for Scenario C are presented in

Table 12. IRR, ROI, and payback period for Scenario C.

Without subsidies	IRR	ROI	Payback period
	5%	6%	16 years
With subsidies
	6%	7%	13.6 years

.

The detailed calculation is presented in Appendix A.

3.5.7. Sensitivity Analysis for Scenario C

Table 13. Results for the sensitivity Analysis for Scenario C.

Without subsidies	IRR	ROI	Payback period
	5%	7%	15 years
With subsidies
	7%	8%	12.7 years

presents the results of a sensitivity analysis, the same kind as for Scenario B. We simulated the increase in purchase price by 15%, while the electricity feed-in tariff decreases by 20%, falling from 0.29 €/kWh to 0.23 €/kWh. The results are comparable to the results of Scenario B.

3.5.8. What-If Analysis for Scenario C

The same market shock (like the COVID-19 pandemic) was applied to the data of Scenario C. The results are presented in

Table 14. Results of the what-if analysis for Scenario C.

Without subsidies	IRR	ROI	Payback period
	3%	5%	19.7 years
With subsidies
	4%	6%	16.7 years

. The results underscore the importance of policy support and financial incentives in mitigating investment risk and ensuring the economic feasibility of advanced smart readiness interventions in commercial buildings.

4. Discussion

The SRI methodology is built on the fundamental assumption that advanced smart systems, such as smart grids, predictive control, and demand-side management (DSM) capabilities, are already available or readily deployable. This predisposition creates a simplified pathway to achieving the highest service levels (e.g., levels 4 and 5), assuming sufficient financial investment. However, in real-world conditions, particularly in such markets as Slovenia, such foundational infrastructure is often limited or entirely absent, making the implementation of these advanced levels significantly more challenging.

For example, in our case study, the SRI tool suggested that 50% of parking spaces be equipped with EV chargers to reach the highest service level in that domain. Yet, this expectation fails to consider the structural, financial, and technical limitations of the building, where the current electrical infrastructure cannot support such extensive charging capacity without major upgrades to both on-site power systems and external grid interactivity.

While the tool provides methodological consistency, it does not account for national differences in infrastructure maturity, energy policy, or investment capacity. This uniformity can disadvantage buildings in less developed markets. To address this, our study introduces the concept of the maximum feasible SRI score, which reflects the highest attainable performance under realistic constraints and offers a more context-sensitive alternative to the idealized maximum obtainable score.

4.1. Defining the Concept of Maximum Feasibility in the SRI Assessment

Verbeke et al., the authors of the SRI methodology, define the maximum obtainable score just as a ratio of the actual impact score and the maximum attainable score [34]. Therefore, the maximum obtainable score is a “by-product” of the scores of independent domains. In this study, we examined the conditions in the building, decided which services could be upgraded according to the SRI methodology, and evaluated these measures financially. A detailed explanation is provided in Section 3.3. Therefore, the foreseen measures could only be implemented if they make financial sense. This means, for example, that we had to skip the service levels in the SRI methodology that suggest grid signals should control water chillers, that the setpoint of heating and cooling and all lighting should be directly dependent on occupancy detection, that electrical energy storage should provide an island operation mode, and that 1300 additional EV charging stations should be added, to name the most prominent. When all these service levels are excluded, we are left with the maximum obtainable score of 60.9%. Therefore, we suggest the score of 60.9% as the maximum feasible score. This score could be achieved without DMS, prediction functions, and smart grids, with an additional investment of 1.38 million EUR.

However, it is important to distinguish between the concepts of the maximum obtainable score and the maximum feasible SRI score. The maximum obtainable score refers to the theoretical upper limit achievable under ideal technical and infrastructural conditions under the assumption of full deployment of all advanced services, including predictive control, demand-side management (DSM) functionality, and grid-interactive technologies.

The maximum feasible score, on the other hand, reflects the highest SRI performance realistically attainable within the specific market, regulatory, and technical constraints of the case study context. In this case, it is grounded in the observed infrastructural maturity and financial limitations present in a specific building sector (in our case, Slovenian). Therefore, to formalize this distinction, we define the maximum feasible score as the upper boundary of the SRI performance given the current constraints in smart grid readiness, building automation capacity, and national policy instruments, whereas the maximum obtainable score corresponds to the full SRI potential as described by the official methodology by Verbeke et al., 2020 [36]. The maximum feasible score is specified for every building individually.

Canale et al. (2024) [45] evaluated two case study buildings in Italy. The authors came to the same conclusion, that 14 functionality levels had to be excluded from the SRI scoring due to being overly ambitious given the current advancements in smart-ready technologies (5 in heating, 2 in DHW, 5 in cooling, 1 in electricity, and 1 in monitoring and control). The maximum feasible scores could, in this case, be determined, and would represent a realistic upgrade pathway with financially viable interventions. The differences between the maximal obtainable SRI score and the maximum feasible SRI score are highlighted in

Table 15. Differences between the concepts of the maximum obtainable SRI score and the maximum feasible SRI score.

Aspect	Maximum Obtainable SRI Score	Maximum Feasible SRI Score
Definition	Theoretical upper limit achievable with full deployment of all the advanced functions	The highest SRI performance realistically attainable under the current constraints
Context sensitivity	Generic and idealized; assumes perfect technical conditions	Context-sensitive; reflects the local infrastructure, policy, and financial limits
Assumptions	Assumes full availability of predictive control, DSM, and grid interaction	Assumes current market maturity and partial deployment of smart systems
Technical preconditions	Full automation and integration with smart grids	Limited to available smart automation and DSM readiness
Infrastructure requirements	Assumes that the infrastructure is fully modernized and interoperable	Based on the observed grid and building system capabilities
Financial viability	Does not account for cost or market constraints	Must be financially viable within the current investment climate
Purpose in the study	Represents the methodological ceiling as defined in the SRI framework	Used as a diagnostic tool to prioritize realistic, high-impact upgrades
Supporting literature	Verbeke et al. (2020) [36]	Canale et al. (2024) [45]

.

4.2. Evaluating the SRI Score; Relevance, Regional Constraints, Occupants, and Infrastructure

The applicability of this case study to other regions depends strongly on the maturity of the smart infrastructure systems and institutional support. In countries such as the Nordic states, Germany, or the Netherlands, where demand-side management (DSM), predictive controls, and digital energy governance are well-developed, the implementation of higher smart readiness indicator (SRI) service levels is far more feasible. These countries rank high in innovation performance consistently, enabling large-scale integration of technologies such as grid-interactive EV charging, automated HVAC systems, and building-level energy storage. Their policy environments also support faster translation of EU-level Directives, such as the EPBD, into practical and funded building upgrades.

In contrast, regions with conditions similar to Slovenia, particularly in Eastern and Southern Europe, face notable barriers, including limited digital infrastructure, fragmented regulation, and constrained financing. As shown in our case, even with policy alignment, technical and financial limitations restrict the deployment of advanced smart systems. The case study building illustrates that the SRI has the potential to play a strategic role, but only if wider systemic issues are addressed. For example, Slovenia’s reform of electricity billing introduces time-based tariffs and penalties for exceeding the agreed capacity, raising the importance of DSM. Meanwhile, outdated grid infrastructure hinders PV integration, with one in four permit applications rejected unless battery storage is included [46]. Eles, the national grid operator, estimates €5.15 billion is needed by 2034 for infrastructure upgrades [47]. Additionally, the SRI’s emphasis on real-time occupancy-based controls may penalize large commercial properties inadvertently, such as malls, hospitals, and airports. These buildings often operate on fixed schedules, or require consistent environmental conditions due to safety or accessibility needs. Implementing granular occupancy detection may be neither cost-effective nor operationally practical, leading to lower SRI scores despite solid energy performance through centralized automation and scheduling.

Lastly, the negative domain score for EV charging in Scenario A (−27.5%) highlights a methodological artifact rather than a punitive outcome. This score reflects the sizable gap between the building’s baseline, limited to five non-networked chargers, and the SRI’s advanced expectations, which assume predictive control and grid interaction for at least 50% of parking spaces. Rather than serving as a penalty, this scoring functions diagnostically, signaling areas with significant improvement potential. It aligns with the broader SRI intention: to identify domains where investment can yield high impact gains in smart readiness, guiding stakeholders toward scalable and cost-effective upgrades.

4.3. Comparison of the Results with Other Studies

The costs of interventions in Scenarios B and C were calculated per square meter of the shopping center (40,000 m²). For Scenario B (from 28.0% to 38.0%), the costs are 165.79 €/m², for Scenario C (from 38.0% to 60.9%)—200.26 €/m². Figure 9 presents these data for scenarios A, B, and C.

Apostolopoulos et al. [28] conducted a study in which they estimated different smart retrofitting scenarios for European residential buildings. The examined case study buildings were from 5 countries (Denmark, the Czech Republic, Greece, Bulgaria, and Austria). The closest location to our case study building was an Austrian multi-family house. The change of the SRI from 4.5% to 28.5% would cost 78 €/m², and from the SRI of 28.5% to 67%, the intervention cost would be 118 €/m². Considering that the case study buildings are different (the Austrian case study is a multi-family building, and our case study is a commercial building/shopping center), obvious parallels can be drawn. The results are coherent. Higher prices can be expected in the commercial building due to the complexity of the systems.

The study conducted by Janhunen et al. [25] found that energy improvements in a shopping center in Finland (100,000 m², upgrades to the photovoltaic power plant, battery storage, active LED lighting, electric vehicle chargers, and an advanced control management system) of €6 million yielded over a 10% return and increased the property value by more than €10 million, supported by a €2 million government subsidy. In our case, the ROI, the IRR, and the payback period are generally more conservative, particularly under non-subsidized conditions. This comparison highlights the importance of localized policy support (e.g., Eko sklad subsidies) to ensure economic viability in transitional markets. The main motif for the energy improvements (according to Scenario B) was the necessity to replace the AHUs due to wear and tear. The possibility of a government subsidy accelerated the decision-making process for the photovoltaic power plant. The process was, in our case, much more pragmatic. The financial analysis highlighted another important point: the EV charging system is expected to last around 10 years, based on typical manufacturer guidelines. However, our calculations show a payback period between 13.2 and 15 years, meaning the investment may only start to pay off after the equipment’s expected lifetime. In our case, larger subsidies would help to reduce the payback period, but there is still a risk that the system could fail or become outdated before it fully pays off.

The results of our study align with previous research by Autio et al. [33]. According to them (based on 49 reports about improving energy efficiency in Finland), energy efficiency improvement projects primarily enhance smart readiness in heating, controlled ventilation, and monitoring/control categories. The examined efficiency improvement projects focus on economic factors, leading to a marginal emphasis on occupant needs and flexibility to the electricity grid.

4.4. The Role of Education and Awareness in the SRI Adoption

While the smart readiness indicator (SRI) framework and the EPBD [5] revisions offer a structured path toward smarter, energy-efficient buildings, their real-world impact depends heavily on stakeholder education, institutional capacity-building, and public awareness. Effective SRI implementation requires a knowledgeable ecosystem involving building owners, facility managers, system integrators, and end users. A study from 2022 [48] highlighted widespread knowledge gaps across Europe. On the one hand, technical expertise is growing, but broader societal awareness remains limited, slowing adoption and weakening the perceived value of digital upgrades, particularly in non-residential buildings.

The European Commission’s Digital Decade Policy Programme 2030 [49] echoes these concerns, calling for improved digital literacy in such sectors as construction and energy. A lack of understanding of predictive control, energy monitoring, or demand-side flexibility not only reduces system utilization, but also undermines investor confidence. These gaps contribute to the persistent “performance gap” between theoretical and actual building outcomes.

Addressing this requires a coordinated educational effort. National SRI implementation strategies should include vocational training for energy professionals and targeted awareness campaigns for building users and decision-makers. Programs such as BUILD UP Skills, co-funded by Horizon Europe [50], and the IEA’s 3DEN Initiative [51], offer strong examples of how education and stakeholder engagement can support the digital energy transition.

Education and awareness must be seen as core elements of smart readiness. Without them, even well-equipped buildings may fall short of policy targets related to energy efficiency and grid responsiveness. Locally, Slovenia has taken steps in this direction through its Strategy for Greening Education and Research Infrastructure [52], supporting workforce development aligned with long-term sustainability goals.

5. Conclusions

In this study, we examined the possibilities for implementing measures according to the SRI calculation methodology in the case of a 2-story shopping mall that underwent modernization. The interventions were evaluated through the SRI methodology. The purpose of the study was to evaluate the retrofitting financially and determine all the measures and conditions necessary to reach the maximum SRI scores. In our study, we showed which measures could be used to follow the requirements of the SRI methodology. Due to the current state-of-the-art, we showed that many measures cannot (yet) be implemented in our case study building. In the feasibility study, we showed that 13 proposed measures in the SRI methodology could not be implemented, because the Slovenian energy grid is not yet at the level of smart grids. The authors of this study [45] in Italy also came to a similar conclusion. All the necessary measures to achieve the maximum obtainable score would almost triple the value of automation equipment in the building. In the study, we proposed using the term maximum feasible SRI score, because not all obtainable service levels are meaningful for our case study building. Our study illustrated that an investment of €6.6 million was required to complete Scenario B. The ROI in this case would be 7%, the IRR—8%, and the payback period—12.3 years (with subsidies). Additionally, €1.35 million would be needed to complete Scenario C and, therefore, the maximum obtainable SRI score. The ROI in this case would be 6%, the IRR—7%, and the payback period—13.6 years. The financial study highlighted that the costs of the interventions according to Scenarios B and C are comparable (considering different types of buildings) to the case study building in Austria [22] and, with limitations, to [25]. In both scenarios, the investments are viable, with a longer timeframe. The analysis highlights the importance of subsidies; without them, the payback period would be even longer. The maximum total SRI score of 100% is not attainable due to technical reasons.

The measures to complete Scenario B were in line with the suggestions of the SRI methodology. It must be emphasized that there are many SRI suggestions that do not make much sense in our particular building, e.g., that grid signals should control water chillers, that the setpoint of heating and cooling and all lighting should be directly dependent on occupancy detection, that electrical energy storage should provide an island operation mode, and that 1300 additional EV charging stations should be added. As soon as the energy network is upgraded to smart grids, many of these suggestions will become relevant. These adaptations are only possible with a budget far exceeding Scenario B.

This case study highlights the practical challenges of achieving high SRI scores in a member state with limited fiscal capacity, modest innovation efficiency, and underdeveloped smart grid infrastructure. These constraints reflect broader implementation gaps within EU policies such as the Renovation Wave [53], the revised Energy Performance of Buildings Directive (2024) [5], and the European Green Deal [54], which often apply uniform metrics such as the SRI without accounting fully for national disparities in infrastructure and institutional capacity. While the proposed interventions align with the overarching EU policy objectives, the financial and technical barriers encountered underscore the need for several targeted policy enhancements. The subsidiary systems (such as Eko sklad in Slovenia) work efficiently in co-financing PV plants, the renovation of heating plants, and the replacement of inefficient AHUs. On the other hand, there is a need for stronger regulatory support for smart grid integration, enabling the deployment of grid-interactive technologies critical for achieving high SRI scores. This is also highlighted in Digitalising the energy system—EU action plan [55].

Data from the European Innovation Scoreboard (2023) [56] and analysis by Andri-jauskienė et al. (2023) [57] showed that countries such as Luxembourg, Sweden, and Denmark outperform others in translating policy into innovation outcomes. In contrast, countries such as Slovenia face greater barriers to deploying advanced SRI functions such as DSM and predictive control. These disparities stem from macroeconomic and structural differences, as explored by Kalapouti et al. (2020) [58] and de Soyres et al. (2024) [16], which affect R&D uptake and building digitalization.

Our case reflects these asymmetries: while some improvements are feasible (e.g., PV integration, EV charging), the absence of grid interaction and predictive systems underlines the limits of feasibility. These findings support recommendations from the JRC and IEA 3DEN [51] initiative, emphasizing that smart readiness must be paired with targeted innovation funding and national capacity-building to avoid reinforcing regional inequalities.

We anticipate that the SRI will soon generate a broader public discussion around the challenges identified in this study. Realizing the full potential of the SRI will require accelerating investments in smart grids, which are a fundamental prerequisite for implementing many of the advanced functionalities outlined in the SRI methodology. Addressing this infrastructure gap is not only necessary for success, but is also central to the mission that the SRI itself promotes.