Comparative Analysis of Design Solutions in Terms of Heat and Electricity Demand with Actual Consumption in a Selected Swimming Pool Facility

Abstract

Facilities with high energy demands, such as swimming pools, face escalating costs in electricity and heating, exacerbated by economic instability and fluctuating energy prices. These facilities are often overdesigned to meet extreme peak demands, resulting in higher than necessary energy usage. Therefore, to reduce costs, diversification of heat sources and tailoring their efficiency to meet real-time needs is required. This study analyzes a swimming pool complex in Poland with a sports pool, a recreational pool, an outdoor pool, and a spa bath, comparing the initial design assumptions for the use of heat and electricity with actual consumption data. By incorporating a mix of energy sources, including cogeneration (combined heat and power), gas boilers, district heating, heat pumps, and photovoltaic panels, the system can flexibly adjust to market energy prices. An automated monitoring system continuously monitors energy use, identifies deviations, and helps pinpoint errors, allowing more precise and economical energy management. Detailed reports generated from meter readings enable comparisons with previous usage periods and guide future planning. A balance of energy production with consumption, adjustment of production to match demand, and configuration of equipment operation with defined parameters all contribute to an effective and cost-effective approach to facility energy management.

1. Introduction

Swimming pools play a significant role in urban energy consumption and media use [1,2]. Their distinctive energy needs, particularly in terms of high heat and electricity loads, differentiate them from other consumers [3,4]. The global increase in energy consumption is driven by factors such as population growth, economic development, and technological advancements [5]. For example, Kampel et al. [6] reported that annual energy consumption in Norwegian swimming pools generally ranges between 1500 and 2000 kWh/m², depending on the type and scale of the facility. Liebersbach et al. [7] estimated that the heating of the facilities, the heating of the water, and the power supply can represent up to 60% of the total operational costs in such facilities. Similarly, Cardoso et al. [8] observed that 66–77% of the energy demand in Portuguese swimming pools is covered by thermal energy, while electricity represents 23–34%. It is imperative to optimize the use of electricity in swimming pools, especially in regions that are highly dependent on fossil fuels for energy generation. Addressing safety and sustainability challenges in various sports facilities, including swimming pools, requires well-informed and tailored interventions in response to escalating energy demands.

Facilities characterized by high energy and heat consumption, such as swimming pools, are currently faced with the challenge of increasing operational costs [6]. Increased expenses associated with electricity and heating, amplified by notable price fluctuations during periods of economic instability, combined with increasing maintenance outlays, particularly prevalent in public facilities, require a proactive pursuit of cost-saving opportunities. Numerous studies confirm the high energy intensity of outdoor swimming pools, which can exceed 2000 to 2500 kWh/m²/year in southern European climates. For example, Mousia and Dimoudi [9] reported an average annual electric energy consumption of 287.34 kWh/m² for open pools in Greece, with thermal energy alone accounting for 2246.59 kWh/m², for a total of 2533.93 kWh/m². Buscemi et al. [10] proposed an advanced predictive model for outdoor pool thermal energy demand, which estimated similar values of 2306 kWh/m² under standard operating conditions. Both studies also highlighted the significant potential for energy savings through simple measures: reducing pool water temperature by 1 °C results in 11–20% lower energy use, while installing a thermal cover at night leads to 25–30% reductions in heating demand. These findings emphasize the importance of precise demand modeling and tailored efficiency strategies, especially in facilities designed without energy recovery or renewable energy systems. This pursuit involves a comprehensive evaluation of cost-effective measures and the implementation of energy-saving strategies to alleviate financial stress while ensuring continued operational efficacy. In indoor swimming pool facilities, significant impacts on energy demand can result from both operational parameters and predictive simulation strategies. Ratajczak et al. [11] demonstrated that simple adjustments in ventilation and heating control, based on in situ measurements, enabled energy reductions ranging from 6% to 47%, without requiring costly infrastructure changes. In turn, Marín and García-Cascales [12] developed a dynamic TRNSYS-based simulation model, empirically validated with a mean prediction error of only 1.77%. Their findings showed that increasing pool water temperature by 1 °C leads to a 9.5% increase in thermal energy demand, underlining the sensitivity of energy consumption to even minor changes in operational setpoints.

Analysis of the demand for heat and electricity in swimming pool facilities, as well as in other large public buildings, has a significant potential to improve sustainability [13]. This can be achieved through the following key contributions:

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Reduce greenhouse gas (GHG) emissions associated with the heating, cooling, and ventilation of the building. Currently, GHG emissions represent approximately 33% of global emissions [14].

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Advancement of energy efficiency and integration of renewable energy sources, such as heat pumps and photovoltaic panels, into building infrastructure, including swimming pool facilities [15,16]. This integration not only serves to reduce energy expenditures but also enhances overall comfort and quality of life within the building [17].

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Facilitation of innovation in the public building sector [18].

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Optimization of energy management within swimming pool facilities.

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Improvement in flexibility and reliability of the operation of the energy plant [19].

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Promotion of spatial and energy planning that takes into account local energy resources and needs, along with environmental and social impacts [20,21,22].

There is insufficient research on energy usage, performance, and operational management in sports facilities, particularly compared to other types of buildings.

Although research on energy efficiency in sports facilities remains more limited than in other building sectors, several recent studies offer detailed frameworks for auditing and performance optimization. For example, Nikolic et al. [23] performed a comprehensive energy audit of an indoor swimming pool facility in Serbia, followed by a multicriteria decision analysis (MCDA) of proposed energy efficiency measures, including pool covers, solar collectors, LED lighting retrofit, CHP installation, power factor compensation and optimization of domestic hot water systems, which together enable approximately 29% reduction in energy consumption and a significant decrease in operating costs. It is necessary to update the design data periodically, based on actual measurements of energy consumption and current usage and technology trends. Without updated standard values for heat, energy, and electrical loads, design calculations become increasingly unreliable, installations are less efficient or redundant, and buildings do not meet modern climate, technical, and cost requirements [23,24].

The authors hypothesize that outdated size indicators and oversized technical systems in public swimming pools lead to inefficient resource use, excessive costs, and unnecessary energy consumption. It is assumed that actual thermal and electrical demands are much lower than design estimates and that hybrid energy systems, combining cogeneration, photovoltaic panels, gas boilers, heat pumps and advanced automation, can improve efficiency and reduce costs. To verify this, a two-year study was conducted in a modern swimming pool complex, comparing design assumptions with measured consumption using recognized energy performance indicators (EPIs) and European benchmarks. Recent literature highlights a gap between predicted and actual energy use in aquatic centers, reflecting the unique challenges of large pools and high humidity [25,26,27].

This study goes beyond general assessments of swimming pool energy efficiency by addressing several research gaps. The novelty lies in: providing a unique 24-month, high-resolution dataset from a Building Management System (BMS), rarely available for aquatic centers; quantitatively comparing design assumptions with actual energy demand, demonstrating oversizing by more than 70%; analyzing the combined operation of a hybrid energy system integrating cogeneration, photovoltaics, condensing boilers, and advanced HVAC with heat recovery; and benchmarking Energy Performance Indicators (EPIs) that are 25–45% lower than European averages, thus establishing new reference values for temperate climate zones. These contributions allow for actionable recommendations in the design and operation of future pool facilities.

2. Materials and Methods

The focus of the case study is to perform a thorough analysis of Aquapark, a swimming pool facility located in the western Polish town, within the Lower Silesian Voivodeship. The architectural structure of the building adheres to a rectangular design, comprising a single-body cuboid that delimits four distinct functional segments: technical compartment, principal hall and sanitary facilities, swimming pool hall, and a wellness area. The general configuration of the building is illustrated in the Ground Floor Layout (Appendix A.1) and First floor Layout (Appendix A.2).

The diagram provided illustrates the various groups of rooms that make up the facility, categorized according to their respective functions (Figure 1).

2.1. Research Approach in the Context of a Case Study

This study uses a case study approach to evaluate the actual performance of a modern swimming pool facility in relation to its initial design assumptions. The selection of this facility was based on the availability of comprehensive project documentation, monitoring infrastructure, and unrestricted access to technical and operational data. This access facilitated a thorough assessment of heating and electricity consumption, including an evaluation of the performance of HVAC systems and various energy sources.

The selected swimming pool facility is a typical midsized aquapark found in Central and Eastern Europe, featuring a sports pool, recreational pool, whirlpool bath, and outdoor pool. It uses a modern hybrid energy system that integrates cogeneration, photovoltaics, gas boilers, and advanced HVAC units with heat recovery. The facility also benefits from an integrated Building Management System (BMS) that ensures continuous and precise data collection, enhancing the reliability of the dataset analyzed. Although this study focuses on a single case, its structure and operational characteristics reflect many public swimming pool facilities. Therefore, the findings and methodologies presented offer insights that may aid in performance assessments, energy benchmarking, and optimization strategies in similar contexts. In addition, a twin facility is under construction, enabling future comparative analyses under different energy configurations. This will provide opportunities to benchmark and validate energy performance indicators in comparable environments.

The methodology integrates a quantitative analysis of the metered energy consumption data with a comparative evaluation against the design values. In addition, energy performance indicators (EPIs) were used to benchmark the facility against others mentioned in the literature. The study also includes an analysis of the impact of weather conditions and building occupancy levels on energy consumption.

2.2. The Ventilation System

The building has been equipped with mechanical ventilation systems to ensure optimal thermal comfort for swimming pool bathers, prevent excessive humidity, and supply the rooms with a sufficient amount of fresh air. Determining the necessary fresh air intake is based on calculations of air flow per person, required air exchanges for each room, removal of harmful substances from the swimming pool area, and absorption of moisture [28,29]. Different areas with varying functions in the building have their own separate ventilation systems. These ventilation systems are designed to provide adequate ventilation and the fresh air required to maintain hygienic conditions. They achieve this through a combination of outside and recirculated air, together with filtration, heating, cooling, and dehumidification processes. The pool area maintains a constant negative pressure to minimize the spread of odors, chemical compounds, and moisture to neighboring rooms. The temperature and humidity levels inside the building are regulated by the pool air conditioning unit, which is equipped with integrated automation and a microprocessor control system for precise regulation of the outside air intake.

Table 1. List of main ventilation systems and their parameters.

System	Area of Operation	Function	Device Description	Efficiency [m3/h]	Communication Protocol
				Air Supply/ Exhaust
NW1	Swimming Pool	Ventilation, heating, cooling, drying	Ventilation unit with single cross-flow exchanger, water heater, heat pump, mixing chamber and integrated automation. Heating capacity—162.0 kW Cooling capacity—105.0 kW	24,800/ 25,550	Individual control panel in the Control cabinet/Modbus MASTER
NW1′	Saunarium	Ventilation, heating, cooling, drying	Ventilation unit with double cross-flow exchanger, water heater, mixing chamber and integrated automation. Heating power—8.0 kW	1540/ 1560	Individual control panel in the Control cabinet/Modbus SLAVE
NW2	Changing rooms	Ventilation	Ventilation unit with hygroscopic rotary exchanger, water heater and integrated automation. Heating power—22.5 kW	4140/ 3690	Control from the ventilation unit SLAVE
NW3	General	Ventilation	Ventilation unit with hygroscopic rotary exchanger, water heater, freon cooler and integrated automation. Heating power—4.9 kW Cooling power—6.7 kW	1895/ 1405	Control from the ventilation unit SLAVE
NW4	Gym, fitness	Ventilation	Ventilation unit with hygroscopic rotary exchanger, glycol heater, freon cooler and integrated automation. Heating capacity—11.7 kW Cooling capacity—22.0 kW. (Additionally, fan coils)	4650/ 4750	Control from the ventilation unit SLAVE
NW5	Technical rooms	Ventilation	Ventilation unit with counterflow exchanger and integrated automation. Heating power—2.7 kW	3660/ 3660	Control from the ventilation unit SLAVE

summarizes all ventilation systems in the facility. A block diagram of the swimming pool hall ventilation system is provided in Appendix A.3.

2.3. The Heat Source

The thermal requirements of the facility are met by a hybrid heat source comprising a series of micro-cogenerators, each possessing a heating capacity of 40 kW, in conjunction with two low-temperature, condensing gas boilers, each with a 280 kW capacity. Monoblock modularity of gas boilers, consisting of 4 independent heat exchangers with a power of 70 kW each in a “monoblock” system, allows for the optimization of power in accordance with the actual heating needs, ensuring maximum efficiency. The boiler room is located atop the building, with a vertical clearance from the floor to the ceiling measuring 2.50 m. The heat source is dedicated to serving the specified heating circuits within the facility, shown in

Table 2. List of main heating systems.

System	Manifold Location	Description	Thermal Power (kW)
System no. 1	Rooftop boiler room	Domestic hot water tanks	220.0
System no. 2	Rooftop boiler room	Distributor in the basement	635.0
System no. 3	Rooftop boiler room	Radiator heating	90.0
System no. 4	Rooftop boiler room	Heaters in ventilation units on the roof	12.5
System no. 5	Basement	Swimming Pool Technology	360.0
System no. 6	Basement	Heaters in ventilation units	210.0

.

The specified capacities for individual heating circuits denote the maximum operational capacities and are contingent upon the facility’s operational mode, whether winter or summer. Moreover, the capacities of the pool technology exchangers integrated into the building’s heat balance are tailored to meet specific operating conditions. It should be noted that the maximum capacities of the exchangers for individual pool basins significantly exceed standard levels, reflecting the increased demand for heat during the initial heating of pool water in these areas. In addition, the heating system was implemented in a two-pipe configuration to accommodate the supply of radiators, water heaters for ventilation units, pool technology, and domestic hot water heating. The allocation of heating mechanisms varies according to the specific room functions. In particular, in the sanitary and hygienic rooms, technical rooms, gym and fitness spaces, and other facilities, the heating provision is designed using steel water plate radiators, thereby ensuring effective and efficient heating solutions. Additionally, designated technical rooms are equipped with electric heating through profiled plate electric radiators, establishing a tailored approach to heating based on distinct spatial requirements. In moisture-prone zones, such as wet rooms, the use of galvanized radiators is recommended to provide corrosion resistance and durability. For the pool hall, associated sanitary and changing room core, heating is facilitated by air derived from the pool central unit, thus ensuring consistent and optimal temperature control. To improve the heating efficiency, the heating system is segmented into circuits based on the designated heating medium. Each heating circuit is furnished with a circulation pump, along with control and shut-off fittings to ensure operational integrity.

Table 3. Thermal energy demand of Swimming Pool Technology.

Swimming Pool Technology System		Thermal Power (kW) **
System no. 1– Sport Swimming Pool	First heating	232.0 *
	Operation	71.0
	Heating water after filter backwash	102.0
System no. 2— Recreational pool	First heating	106.0 *
	Operation	21.0
	Heating water after filter backwash	60.0
System no. 3— Whirlpool bath	First heating	14.0
	Operation	3.0
	Heating water after filter backwash	32.0 *
System no. 4— Outdoor pool	First heating	521.0 *
	Operation	369.0
	Heating water after filter backwash	432.0

* The power for which the exchangers have been selected. ** Circulation calculations and water change frequencies were performed as per DIN 19643 [30]. Thermal power demand and partition losses were evaluated according to PN-EN 12831-1:2017-08 [31], heat losses, including evaporation, were estimated according to VDI 2089 Blatt 1 [32].

shows the heat demand of individual swimming pool systems.

2.4. The Energy Requirement of the Facility

The Aquapark facility was equipped with a 630 kVA container transformer station located adjacent to the building. This station supplied power to all the circuits within the Aquapark, which included external and internal lighting installations, electrical switchboards that support the operation of all devices in the facility, Building Management System (BMS), Electronic Customer Service System (ECSS) installation, photovoltaic, fire protection, and surge protection. The designed computing power of all the facility circuits is presented in

Table 4. Power balance.

Receiver	Processing Power (kW)	Simultaneity Factor	Assumed Power (kW)
Interior lighting (LED)	13.6	0.8	10.9
Outdoor lighting	0.7	1	0.7
General inspections	49.9	0.4	20.0
Computer pickups	5.4	0.7	3.8
Ventilation, air conditioning, heating	150.0	-	110.0
Telecommunication engineering	26.0	1	26.0
Dryer sockets	15.0	0.6	9.0
Buffet	4.5	0.6	2.7
Water technology	160.0	0.9	144.0
Hydrophore	12.0	0.5	6.0
Elevator	8.0	0.4	3.2
Saunarium	73.0	0.7	51.1
TOTAL	445.1		387.3

.

The computing power assumed at the design stage was 348.6 kW, assuming a simultaneity factor of 0.9. A simultaneity factor of 0.9 has been applied to the total installed capacity to reflect that not all electrically powered systems operate at full load simultaneously. This approach is consistent with standard engineering assumptions and aligns with the methodology outlined in PN-EN 16798-1:2019-06 [33], which recommends the use of diversity factors to estimate the actual energy demand in HVAC and electrical systems.

The building has been equipped with photovoltaic and cogeneration systems to meet its energy requirements. Specifically, photovoltaic panels with an approximate capacity of 7600 W have been strategically installed on the roof, while a cogeneration system comprising 3 XRGI 20 cogenerators (GHP Poland, Gliwice, Poland), each with an electrical capacity of up to 20.0 kW, has been incorporated within the building. It is important to note that the energy generated by these systems is utilized solely for the building’s internal consumption, and there are no plans to supply surplus energy to the city grid. The photovoltaic installation has recently undergone an expansion (January 2024) with the addition of new collectors, for a total capacity of 40.0 kW.

2.5. Methodology of Measurements, Measurement Period, Samples and Data Sources

All equipment and systems within the facility are integrated with advanced BMS, which continuously aggregates data and governs the operation of the entire system [34]. The system has the ability to manage energy through interactive graphical interfaces, facilitating visualization of energy consumption, implementation of energy plans, and comparative analysis of energy consumption across preceding billing periods. The parameters utilized for these purposes are derived from meter readings, data-providing devices, and tariff information furnished by the facility manager.

Table 5. List of measuring equipment.

Device	Type	Producer	Measurement Value	Accuracy/ Resolution	Communication Protocol
Frequency converter	CFP2000	Delta Controls (Kraków, Poland)	Hz	±0.1 Hz	PROFIBUS DP
Heat meter	Hydrosplit M3 HYDROCAL M3	B METERS SRL (Psary, Poland)	GJ	±2% (MID B), errors Err101–Err108	M-BUS
Water meter	GMDM-I WDE-K50	B METERS SRL (Psary, Poland)	m3	±2% accuracy/±0.5% repeatability	M-BUS
Electromagnetic flowmeter	FM-300	TECHMAG S.C. (Gliwice, Poland)	m3/h	±2% accuracy/±0.5% repeatability	4–20 mA
Energy analyzer with current measurement	NMID30-2	LUMEL (Zielona Góra, Poland)	kWh	1% (Class B), frequency range 45–65 Hz	Modbus RTU
Temperature sensor	22DT-14L	BELIMO S.A. (Warszawa, Poland)	°C	±0.5 °C at 21 °C	4–20 mA
Cogenerator	MCHP XRGI20	GHP Poland Sp. z o.o. (Gliwice, Poland)	kWh m3	±1–2%	MODBUS

lists all measuring devices used for the energy analysis of the facility.

Measurement data was continuously collected over a 24-month period from July 2022 to July 2024. The analysis was based on actual operational data obtained from the Building Management System (BMS), which integrates real-time information from all relevant utility meters. The scope of the data collected comprised:

• hourly and monthly values of electricity and thermal energy consumption,
• flow rate and temperature measurements in swimming pool circuits and ventilation systems,
• actual facility occupancy data along with visitor statistics,
• outdoor air temperature data recorded by local weather stations.

To evaluate the reliability of the measurement data, a simplified cumulative uncertainty analysis was performed for the primary measuring devices used within the facility. This analysis employs the Root Sum Square (RSS) method of summary uncertainty, which incorporates several critical factors:

• The accuracy of the devices as specified by the manufacturer.
• The measurement resolution, defined as the smallest increment in reading that can be accurately detected.
• Potential errors arising from data transmission and conversion within communication systems, including the M-Bus, Modbus, and 4–20 mA protocols.

The analysis specifically addressed measuring devices, concluding that the estimated uncertainty falls within the range of ±1.5% to ±3.5%, contingent upon the type of sensor and the measured parameters. This methodology ensures sufficient accuracy to perform comparative and trend analyses at the facility level. The uncertainty analysis was performed using the Root Sum Square (RSS) method, combining the manufacturer’s specified accuracy, sensor resolution, and potential data transmission errors (M-Bus, Modbus, 4–20 mA). The resulting uncertainty range of ±1.5–3.5% reflects differences between device types (e.g., flowmeters vs. energy analyzers). This level of uncertainty is considered sufficient for trend analysis and comparative evaluation of energy performance and is consistent with the practices of ISO 50001-based [35] energy audits.

The analysis assessed both total energy consumption and detailed Energy Performance Indicators (EPIs), which are elaborated in Section 3.2, thereby enabling standardized comparisons. All data was verified against facility operating records, as well as service and maintenance documentation. Accurate sensor calibration is critical for reliable building energy monitoring. Recent developments in in situ calibration and autoencoder-based techniques facilitate ongoing fault detection and recalibration without requiring prior knowledge of faults [36,37,38,39]. These methods improve the precision of thermal and flow measurements in heating, ventilation, and air conditioning (HVAC) and pool energy systems, supporting more robust benchmarking and optimization analyses.

2.6. Analytical Evaluation and Benchmark Selection

Data analysis was performed using spreadsheet-based tools and GCL+ (General Control Language Plus) for control logic. GCL+ facilitated the evaluation and visualization of time series data, as well as the management of building automation communication systems. To evaluate system performance and identify deviations from design assumptions, a set of comparative indicators was used, including:

• percentage and numerical deviations between projected and actual energy consumption,
• seasonal variability of energy demand,
• distribution of energy consumption in functional systems, such as ventilation, pool heating, building heating, and electrical consumption by appliances,
• share of individual energy sources, including cogeneration, photovoltaics, gas boilers, and grid power.

The results obtained were compared with data from the literature, referencing studies by Cardoso et al. [8], Smedegård et al. [40], and Yuan et al. [41].

The benchmarking facilities were chosen based on peer-reviewed studies that offer comprehensive energy performance indicators (EPIs) for swimming pools of a similar scale. Facilities were included if standardized EPI values were available per usable area, water surface, or visitor, and if both thermal and electrical energy demands were reported. Additionally, the selection covered multiple European climate zones, including Portugal, Norway, Finland, and Serbia. This approach ensures scientific rigor, enhances comparability, and situates the Polish case facility within an international scientific context.

3. Results and Discussion

Swimming pools require the provision of optimal thermal comfort conditions, including adjustments in temperature and relative humidity, to accommodate the occupants and counteract inherent heat losses [42,43]. Consequently, these facilities, predominantly under municipal ownership, bear a considerable financial burden in terms of energy consumption, predominantly electricity and heating. Research conducted, among others, by Liebersbach et al. [7] has highlighted that maintenance expenses, including space heating, water heating, and power consumption for swimming pool facilities processes, constitute nearly 60% of total operational costs. Therefore, this study aims to meticulously examine the energy consumption patterns of these establishments.

3.1. Distribution of Energy Consumption

Cardoso et al. [8] demonstrated a significant disparity in energy consumption between thermal and electric energy uses in the five sport complexes with indoor swimming pools, located in the cities of Coimbra and Leiria, in the Central Region of Portugal. Their analysis revealed a greater dependence on heating (thermal and energy) in all five of the studied cases of swimming pools. The consumption of natural gas for heating purposes was in the range of 66% to 77% of the total energy consumption, while electricity accounted for between 23% and 34%.

The energy delivered to the swimming facility located at Jøa (an island in the municipality of Namsos in Norway) investigated by Smedegård et al. [40] was almost evenly divided between electricity (64%) and thermal energy (46%). For the Aquapark analyzed in this study, annual heat consumption for heating purposes was equal to 59%, while annual electricity consumption was 31%. Monthly electricity consumption ranges from 35% up to 52% while monthly heat consumption varies between 48% and 67% (Figure 2).

The research findings for the facility in Poland revealed that the demand for thermal energy is slightly lower compared to the findings from five different locations in Portugal [8]. This difference is surprising considering the climate variations between these two countries. In particular, the modern Aquapark, which became operational in 2022, is equipped with advanced sustainable features such as air recirculation and heat recovery systems, cross-flow heat exchangers, and adaptive control of technological devices based on humidity levels in the swimming pool hall. On the contrary, the facilities examined in Portugal were established between 1991 and 2005, with the two oldest ones submitted to requalification in 2014 and 2003, respectively.

When comparing the outcomes obtained for Poland and Norway and noting the utilization of similar technological solutions in heat recovery, distinct disparities emerge. In Poland, the heat demand surpasses the electrical demand, while the reverse holds true for Norway. This dissimilarity is primarily attributable to an outdoor swimming pool within the Lower Silesian water park complex. Notably, the heat demand from the pool represents a substantial 18% of the total heat demand (Figure 3), despite its seasonal operation limited to three months annually. Furthermore, the analyzed swimming pool facility features a sauna zone powered by electric heaters, not implemented in the Norwegian facility.

According to the design assumptions specified in Swiss planning basics [44], it is observed that the cover for the heating of the swimming pool water encompasses 24% of the total heating consumption. Saari and Sekki [45], who analyzed a swimming pool facility located in Kirkkonummi, located in the Greater Helsinki area, described a similar design value (30%). The actual measurements made by Yuan et al. [41] or another pool, also in Helsinki, have shown the value of 25% (669 MWh/year). On the contrary, an analysis conducted within this study (Figure 3) reveals that the water heating of the swimming pool involves a consumption of 5426.8 GJ (1507.4 MWh) during the 24-month period under review, representing close to 82% of the total energy consumption 6649.3 GJ (1847.0 MWh) divided into four individual swimming pools. WT1 2235.24 GJ (34%), WT2 1734.71 GJ (26%), WT3 265.45 GJ (4%), WT4 1191.40 GJ (18%). The average annual demand for the heating of swimming pools in the analyzed Aquapark (753 MWh/year) closely parallels that of Helsinki (669 MWh/year) [41]. However, the distribution of this demand as a percentage of the total varies significantly between the two locations. This disparity arises from the notably higher energy consumption for purposes other than the heating of swimming pool water in the Helsinki facility. In contrast, at Polish Aquapark, the utilization of media recovery technologies results in a negligible requirement of heat for other purposes. Smedegrd [40] demonstrated a slightly lower share of the heat demand for swimming pool technology (65%) was demonstrated by Smedegård [40] for a facility similar to those of Aquapark in Poland.

During the review period, thermal energy consumption exceeded electricity use, comprising 48% to 67% of total energy consumption. There were notable increases in heating demand during winter for building heating and summer for outdoor pool heating, while electricity consumption remained stable (Figure 2).

Theoretical propositions by Kannewischer [44] indicate an anticipated domestic hot water use of up to 25%. Consequently, in Finland, Saari and Sekki [45] posit a similar estimate of 26% for Kirkkonummi and Yuan [41] 36% for Helsinki. Smedegård et al. [40] have demonstrated an actual value of 17% for a swimming pool located in Norway. Monitoring of the analyzed Aquapark over 24 months revealed a modest domestic hot water usage of 115 GJ, resulting in a mere 2% of the total energy consumption (Figure 3). In contrast, the findings of a separate study in Poland [46] suggest that domestic hot water consumption represents the largest proportion of thermal energy required for heating, possibly up to 39%. Several factors may explain these discrepancies. First, advanced heat recovery systems and a BMS-based automation strategy reduce domestic hot water (DHW) demand by optimizing setpoints and reusing waste heat. Second, the energy allocation methodologies vary: this study attributes more thermal load to the heating and ventilation, while others assign a large share to the DHW. Third, the operational profile of the case study, with a large water surface area, means that pool heating dominates the energy balance and reduces the relative contribution. Finally, design assumptions based on peak loads and worst-case scenarios tend to overestimate DHW demand compared to actual conditions. These differences highlight the need for empirical monitoring and system-specific allocation when analyzing energy consumption.

The heating of the swimming pool water comprised 82% of the thermal energy consumption, primarily due to sports and recreational pools. In comparison, other systems such as ventilation, radiators, and domestic hot water constituted a significantly smaller share (Figure 3).

Other areas of technological heat use are central heating and ventilation, which constitute 8% and 9% of the total thermal energy demand, respectively. For a sports center located in Helsinki studied by Yuan et al. [41], the supply air heating was 23% of the total heat demand, and the space heating was 17%. The energy distribution for the swimming pool studied in Norway [40], the thermal load of the air handling unit (ventilation) was 17%, while the central heating demand was not taken into account. Wnukowicz [29] found that using an air conditioning unit with variable recirculation, controlled based on the need to maintain constant air humidity, only consumes approximately 11% of the heat compared to a ventilation unit without recirculation to ventilate a swimming pool hall annually. In addition, using an air conditioning unit that automatically increases air humidity at night reduces ventilation heat losses by approximately 13% compared to units that do not have this feature. The implementation of environmentally sustainable solutions at the Aquapark analyzed has markedly reduced the heat consumption required to maintain thermal comfort within the swimming pool hall in contrast to analogous facilities in Europe. The heat energy distribution for the ventilation system of the subject facility is delineated in Figure 4. During the 24-month monitoring period, the cumulative thermal energy demand for this purpose amounted to 591.84 GJ (164.4 MWh), with an annual average of 295.92 GJ (82.2 MWh). Almost 54% of this value can be attributed primarily to the ventilation center of the swimming pool hall, responsible for maintaining a consistent temperature range of 30–32 °C and a humidity of 55%. Notably, the analysis indicates that 317.9 GJ (88.3 MWh) of thermal energy were expended for this purpose during the monitoring period, resulting in an annual average of 159 GJ (44.15 MWh).

The primary sources of power consumption within the Polish Aquapark analyzed in the presented study were identified as water treatment, accounting for 26%, and ventilation system, representing 13% of total power consumption. The detailed characteristics of the electrical demand for these two purposes are presented in Figure 5.

The pool hall accounted for 54% of the ventilation heat demand, highlighting its importance for thermal comfort. In comparison, other areas showed lower consumption, suggesting opportunities for targeted energy savings (Figure 4).

When evaluating the design assumptions [7,44], it was determined that ventilation systems accounted for approximately 36% of the power consumption, while the water treatment system accounted for 18%. The deviation between actual electricity use and anticipated values and design assumptions underscores the imperative to integrate sustainable solutions within the analyzed facility. Smedegård [40], who investigated a facility employing sustainable technical measures, reported comparable findings. Furthermore, Saari and Sekki [45] observed percentages of power distribution of 27% for air ventilation and 30% for technological pumps.

The analyzed Polish aquapark exhibited a lower heating demand than comparable facilities in Portugal, primarily due to the implementation of advanced, energy-efficient solutions such as heat recovery systems and reversible heat pumps. Climatic conditions are a decisive factor, while Portugal’s Mediterranean climate generally requires lower heating requirements, the temperate continental climate would typically impose higher loads. However, the Polish facility demonstrates reduced overall energy use as a result of effective energy optimization. In contrast, facilities located in Northern Europe (e.g., Finland and Norway) experience substantially higher heating demand due to prolonged and severe winter conditions. Additional factors, such as insulation standards, building codes, recent energy retrofits, occupancy profiles, and the presence of heat reuse technologies, further influence thermal performance. These variables must be taken into account when comparing energy demand between facilities located in different climatic and operational contexts.

Most of the electric energy used in the swimming pool facility comes from the ventilation system in the swimming pool hall, which takes up 60% of the total. Water technology systems also use a lot of energy, with the sports pool accounting for 36% and the recreational pool for 27%. These results show where we can focus on energy-saving measures (Figure 5).

3.2. Analysis of Energy Performance Indicators (EPIs)

Energy performance indicators (EPIs) serve as quantifiable measures for evaluating energy performance, as described by Wang et al. [47]. Among the various metrics used to assess different building types, energy use intensities (EUI) expressed in kWh/m² are commonly employed. Kampel et al. [6,48] conducted a study involving 43 Norwegian swimming facilities and determined that a direct comparison of annual media consumption in swimming pools, considered energy-intensive building types [49] is not suitable. As an alternative, they proposed conducting a performance analysis based on prevalent energy performance indicators (EPIs) found in the literature, such as energy consumption per usable facility area and energy consumption per water surface area. Kampel [50] found that visitors are the single variable that explains most of the variation in the energy performance of swimming facilities. This suggestion is in accordance with the process of identifying swimming facilities within industrial plants [51].

Table 6. List of EPIs used in swimming pool facilities based on Kampel [50] and Kampel et al. [48].

EPI	Index	Description	Explanation
EPIUA	UA	Usable Area (kWh/m2 UA)	Usable area of the facility, calculated on the basis of ISO 9836:2017 [52]
EPIWS	WS	Water Surface area (kWh/m2 WS)	The parameter is calculated from the circumference of the swimming pool basin, excluding any additions such as slides or water attractions.
EPIWU	WU	Water Usage (kWh/m3)	The volume of water utilized within a facility over the course of a year is closely associated with the number of bathers.
EPIVis.	Vis.	Visitors (kWh/visitors)	Average number of visitors per year.
EPIYOH	YOH	Yearly operating hours (kWh/h)	The annual cumulative operating hours of a specific facility.
EPIHDD17	HDD17	Heating degree days with base temperature of 17 °C	A parameter closely correlated with the climate zone in which the facility is located. Specifically, we are referring to space heating using a base temperature of 17 °C.
EPIAWT	AWT	Average Water Temperature (°C)	The expected energy use is directly proportional to the temperature of the pools.
EPIAge	Age	Age of facility	The age of buildings serves as a general indicator of the gradual decrease in efficiency of the installed technological systems over the building’s lifespan.

describes the EPI most commonly used in swimming pool facilities.

The study carried out by the Portuguese research team Cardoso et al. [8], compared a swimming pool with a similar water surface and usable area (CPRA) to the analyzed complex. The findings revealed the following indicators: EPI_UA = 300 kWh/m², EPI_WS = 1400 kWh/m², EPI_Vis. = 14 kWh/visitor (heat demand), EPI_UA = 175 kWh/m², EPI_WS = 750 kWh/m², EPI_Vis. = 7.5 kWh/visitor (electricity demand). The Norwegian team’s investigation of the facility in Jøa revealed an energy value of EPI_Vis. = 44.8 kWh/visitor [40]. The average energy consumption for swimming pools in Norway is 26 kWh per visitor, with a range from 10 to 80 kWh per visitor. Anne Sofie Abrahamsen and Marius Bergh’s 2008 report on energy consumption by swimming pools in Norway [53] notes an energy consumption value of 299.4 kWh/m² in a heated area. Yuan et al. [41] have documented consistent heat demand values per square meter of usable area. Specifically, the reported value was 340.4 kWh/m², increasing to 4725.2 kWh/m² per square meter of water surface. The considerable demand for water surface area per square meter is attributable to swimming pools, which represent only 22.2% of the entire facility’s area. The Saari and Sekki’s team [45] conducted an analysis of the same indicators for the Kirkkonummi pool. The respective values obtained are as follows: EPI_UA = 396 kWh/m², EPI_WS = 2784 kWh/m² for annual heating energy and EPI_UA = 240 kWh/m², EPI_WS = 1691 kWh/m² for annual electrical energy. In the context of the Wrocław complex, the thermal energy value per square meter of usable area amounted to 278.5 kWh/m², consistent with the findings of other research teams. In particular, the EPI_WS value of 1093.8 kWh/m² represents the lowest achieved value, 21.9% lower than the minimum value obtained by the team in Portugal. These energy savings are primarily attributed to heat recovery and the utilization of a central filter backwash system with water, along with heat recovery from the backwash water. The procedures implemented have effectively led to a reduction in heat demand. Regarding the demand for electricity, commensurate values that were in accordance with those of the complex in Portugal were derived. For the facility in Poland, the corresponding values were: EPI_UA = 192.3 kWh/m², EPIWS = 755.4 kWh/m². The heat and electricity demand generated per visitor in the analyzed Aquapark is considerably lower compared to that of all the other analyzed teams and the statistical average for Norway and amounts to: EPI_Vis. = 3.4 kWh/visitor (electricity demand) and EPI_Vis. = 4.9 kWh/visitor (heat demand). This disparity is primarily attributed to differences in facility attendance. Specifically, the number of visitors per month in Jøa is 235 visitors [40], while in Portugal it is 6250 visitors per month (75,000 per year) [8]. On the contrary, the average of the analyzed Aquapark stands at 17,756 visitors per month. Moreover, outdoor climate may account for variations in this performance indicator, as the data have not been adjusted for climate conditions.

A comparison can be made between the indoor swimming pool facilities in Kragujevac, Serbia, as analyzed by Nikolic et al. [23], and Wrocław, Poland. The Kragujevac facility, built in 2011, exhibited higher levels of thermal energy demand, with 353 kWh/m² UA and 1600 kWh/m² WS, and lower electricity demands of 122 kWh/m² UA and 553 kWh/m² WS. An energy audit identified a possible 29% reduction in total energy consumption through six energy conservation measures (ECMs), including pool covers, solar thermal systems, LED lighting retrofits, and a combined heat and power (CHP) system. Conversely, the Wrocław facility, operational since 2022, incorporates advanced heat recovery systems and building automation, resulting in lower energy intensity values of 278.5 kWh/m² UA and 1093.8 kWh/m² WS for thermal energy. However, a lack of a seasonal cover for the outdoor pool can increase the seasonal heat demand. In terms of electricity usage, Wrocław recorded higher values, reaching 192.3 kWh/m² UA and 755.4 kWh/m² WS. This is mainly due to the extensive wellness and sauna area, equipped with electric heaters, along with a gym that contributes to overall electricity consumption. These results demonstrate the effectiveness of modern design strategies and the appropriate system size implemented during the early planning stages of both facilities. A study by Ratajczak [11] assessed the use of decentralized ventilation systems with integrated heat pumps in indoor swimming pools. This proposed solution achieved energy savings of up to 38% compared to centralized systems. The savings were mainly attributed to enhanced heat recovery efficiency, which reached 92%, as well as demand-based operational control. In the case of the Polish facility analyzed, although it utilized a centralized ventilation system, it still attained similarly favorable Energy Performance Indicator (EPI) values. This was made possible through the integration of high-efficiency heat recovery, variable airflow regulation, and building automation. This indicates that well-designed centralized systems, with smart control logic, can provide performance comparable to decentralized solutions, since optimization is integrated into both the design and the operational phases. Predictive optimization has emerged as a central research area in building energy management, surpassing traditional automation approaches. Techniques including model predictive control (MPC), long short-term memory (LSTM) based forecasting, and hierarchical demand response strategies facilitate dynamic regulation of heating, ventilation, and electrical loads in response to real-time operational data [54,55,56]. Implementing these advanced control methods in hybrid swimming pool energy systems can enhance operational efficiency and support integration with the electrical grid.

EPIs are essential for evaluating the operating costs of facilities and should inform design guidelines aimed at minimizing environmental impact and reducing energy consumption. A low EPI reflects high operational efficiency and the need for modern recovery technologies. The EPIWS of a Polish facility is recorded at 1093.8 kWh/m²/year, significantly lower than the European average of 1500 to 2000 kWh/m²/year. This results in energy savings of 25% to 45%, translating into annual savings of 365 to 815 MWh, or €43,870 to €97,870 at an energy price of €0.12/kWh. In contrast, a Finnish facility shows an EPIWS value of 4725.2 kWh/m²/year, leading to an annual difference in energy consumption of more than 3.26 GWh and potential savings exceeding €390,000. These differences emphasize the importance of effective design and technology, including heat recovery systems, optimized HVAC solutions, proper thermal insulation, and advanced automation. A lower EPIWS not only reduces operating costs but also reduces CO₂ emissions, supporting climate objectives and the sustainable development of sports and recreational infrastructure. Benchmarking facilities in Portugal, Norway, and Finland provides a relevant comparative context; however, the results lack full normalization for climate and occupancy differences. Variations in heating degree days and seasonal occupancy substantially influence energy performance indicators. Facilities located in colder climates exhibit increased heating demand, while higher visitor numbers reduce the energy performance indicator (EPI) per visitor. Consequently, this study presents relative rather than absolute cross-country comparisons. To achieve more robust comparability, future research should implement climate-adjusted normalization and standardized occupancy metrics to facilitate more precise international benchmarking. This study is limited by its focus on a single facility, the Wroclaw Water Park, which constrains the generalizability of the findings. However, given the challenges of obtaining reliable operational data from swimming pools, a detailed case study still provides valuable information. The planned completion of a Sister Facility will enable future multi-case research and support the broader application of these conclusions.

3.3. Analysis of Energy Sources

The dominant source of heating in Poland remains coal, which constitutes 74% of the energy supply [7,57]. This dependency significantly contributes to atmospheric CO₂ emissions and exacerbates the greenhouse effect. Given the substantial energy demands of swimming pool facilities, prioritizing energy recuperation, heat recovery, and integration of renewable and environmentally sustainable systems is imperative [58,59]. The Lower Silesian water park is equipped with the installation of three micro-cogenerators, as detailed in Section 2, in addition to condensing gas boilers, photovoltaic panels, a passive heat recovery system from swimming pool systems, and a heat recovery from recirculated air in ventilation units. The high energy demand of the swimming pool facility makes it an optimal candidate for combined heat and electricity production [7,18]. It is imperative to emphasize that this approach necessitates the concurrent capture of both parameters. Thus, configuring the operational mode of the energy source to maximize production capacity is of paramount importance.

Figure 6 and Figure 7 illustrate the monthly distribution of energy and heat sources in relation to total power and thermal demand. Figure 6 shows the share of electricity from cogeneration units, photovoltaic panels, and the municipal power grid, while Figure 7 details the thermal energy sources, specifically cogeneration and gas boilers. These diagrams are essential for understanding the facility’s operational dynamics and energy management strategy. They reveal seasonal variations, indicating a significant reliance on cogeneration during winter, supplemented by gas boilers. In summer, the contribution of photovoltaic panels increases, while the utilization of cogeneration decreases due to lower heat demand. The figures also highlight instances of energy source misconfiguration, particularly in July 2022, when external supply reliance was predominant. This visual analysis helps in assessing efficiency, source prioritization, and operational alignment with energy demand.

When reviewing Figure 6, it becomes apparent that the energy demand of the facility stretches from July 2022 to July 2024. In particular, during the winter months, an optimal proportion of thermal energy production (as evidenced in Figure 7) is demonstrated in relation to electricity (Figure 6). Specifically, cogeneration effectively satisfies approximately 60% of the demand for electricity building in this period, attaining values ranging from 28.0 to 35.9 MWh (54–65%), while concurrently addressing the heat demand at a level of 56.5–70.0 MWh (54.7–72.5%) of the total building demand, as indicated in Figure 7. It is imperative to acknowledge that the average production capacity nears 44 MWh of electricity (equating to 60 kW of modulated electrical power) during the same months. Furthermore, the average thermal energy production capacity is 87.7 MWh (equivalent to 120 kW of thermal power).

Notably, the apparent underutilization of the cogeneration cascade’s production capacity is attributed to the disparate distribution of demand for electrical and thermal energy. Increased electricity demand occurs in the evening hours when the sauna zone is open and classes are held in the gym and fitness zone. On the contrary, the heat demand is consistent throughout the day, with peaks during filter rinsing periods by pool staff, typically conducted after facility hours. Consequently, the change in the demand curve impacts the distribution irregularities. Following the installation in January 2024 of additional photovoltaic panel modules, the share of energy derived from renewable sources notably rose to 13% in the summer. July 2022 is an important month for analysis, as the majority of energy was sourced from the city network. This was influenced by the misconfiguration of the energy source, primarily set for supply through gas boilers, by the higher temperature setting.

As a result, gas consumption from the network accounted for 55.8 MWh (75.5%) of the heat demand and 53.1 MWh (83.9%) of the electricity demand. Such a configuration is undesirable due to the elevated costs and the absence of combined production. During the analyzed period, November 2023 emerged as the most favorable month. In this month, we were able to meet almost all the demand for heat energy (100% through cogeneration) and nearly 50% of the electricity demand. In the analysis presented in Figure 8, the authors investigated the correlation between the operation of photovoltaic panels and the energy consumption of the city network. The graphical representation effectively illustrates the alignment of the photovoltaic curve with the city network energy consumption curve. This indicates that photovoltaic installation prioritizes the use of solar energy during daylight hours, with city network energy serving as a backup during periods of limited solar energy. It is worth noting that the text omits the proportion of energy supplied by the cogeneration system, which satisfies the facility’s constant energy requirements.

The authors’ analysis of energy distribution and prospective system automation encompasses the temporal distribution of heat consumption within distinct seasonal periods: autumn-winter (Figure 9) and spring-summer (Figure 10). During winter, the increased demand for heat exceeds the capacity of the cogeneration system, requiring supplementation via gas boilers. On the contrary, the summer phase exhibits spikes in heat demand, primarily attributable to the operation of the outdoor pool [10]. The filling and heating of the outdoor pool result in a surge in heat demand at the end of May, followed by the primary heating period in early July. Subsequently, the heat demand decreases, remaining below production capacity, until a substantial increase in September, associated with outdoor pool operation. Following the pool’s emptying at month-end, a pronounced reduction in heat demand is observed. The future transition involves redirecting the heat demand originating from the outdoor pool to accommodate the heating requirements of the building. In response to seasonal and daily fluctuations in energy demand, a preliminary energy management strategy has been established to optimize the use of local energy sources, particularly the cogeneration system. This strategy assumes the adjustment of the heating equipment schedules to align with anticipated peaks in thermal and electrical demand. During the peak hours of the afternoon and evening, when energy consumption increases due to high use of the sauna, the pool water temperature will increase by approximately 2 °C above the reference value. The excess thermal energy will be stored in a large-capacity buffer, allowing the simultaneous operation of all cogeneration units and improving overall efficiency. At night, the settings for certain tanks will be lowered to facilitate cooling, in sync with the morning filter backwash cycle, which increases demand for electricity and heat. This alignment will allow the congenators to be restarted in coordination with the facility’s operational cycles. Future development of this strategy will rely on observations of actual utility consumption and equipment schedules, thus verifying assumptions and optimizing energy source operations through predictive control models.

Figure 6 and Figure 7 illustrate the sources of electricity and heat, respectively, that contributed to the Aquapark’s total energy demand from July 2022 to July 2024. The cogeneration system was the primary electricity source, providing up to 60%, supplemented by photovoltaic panels in summer (up to 13%) and grid power. For heat, the cogeneration system consistently met most of the thermal demand, with gas boilers serving as a secondary source during peak periods. Despite a stable installed capacity, the cogeneration system was not fully optimized throughout the year, revealing potential for improved efficiency and energy balance.

The operation of photovoltaic panels demonstrates a distinct daily production cycle. During the night and periods of low solar availability, the electricity from the grid (Tauron S.A.) primarily meets demand. This inverse relationship indicates that solar energy is prioritized, while the municipal network acts as a supplementary source (Figure 8).

During autumn and winter, daily heat demands often exceed the maximum output of cogeneration systems, particularly from December to early February. This highlights the need for supplementary heating, such as gas boilers, and underscores the importance of dynamic operational planning to ensure efficient heating during peak periods (Figure 9).

During spring and summer, the heat demand generally remained below the cogeneration capacity, except for brief peaks in June and September due to the filling and heating of the outdoor pool. This suggests that cogeneration can largely meet heat requirements throughout the warm season (Figure 10).

3.4. Comparison of Design Solutions in Relation to Actual Consumption

Based on a comprehensive two-year study conducted from the facility’s launch in July 2022 to August 2024, an in-depth analysis of the facility’s heat and electricity demand was undertaken. The minimal recorded heat demand of 56.5 MWh occurred in November 2022, whereas the maximum demand of 117.6 MWh, equivalent to 158.1 kW, was observed. The median heat demand during this period remained stable at 87.9 MWh. The designed heating power was initially estimated to be 895 kW. However, a revised value of 680 kW was adopted to suit the specific requirements of heating the swimming pool water and maintaining the water parameters during regular operations. In particular, the average monthly demand during the coldest month, January 2024, at an average monthly temperature of −2 °C, peaked at 117.6 MWh, representing 23% of the designed demand value. Although the existing facility operates with a high degree of technical complexity, our analysis indicates that several aspects of the original design are over-dimensioned in relation to actual energy demand. The installed heating capacity exceeds the observed peak demand by more than 70%, suggesting that a more tailored design, based on dynamic demand modeling and user occupancy forecasts, could have yielded a more cost-effective and energy-efficient solution. Such comparative insights underline the importance of reevaluating current design practices in future swimming pool projects.

The analysis of the electricity demand for the facility yielded a calculated design value of 348.6 kW. On a thorough examination of the market data, it was found that the average demand during the specified time frame was 60.9 MWh (equivalent to 81.9 kW). Notably, the minimum recorded value in April 2024 amounted to 47.7 MWh, while the maximum demand peaked at 76.6 MWh in July 2024. It should be noted that the maximum demand only reached 30% of the design value.

To illustrate the practical implications of the discrepancies between design assumptions and actual energy demand,

Table 7. Comparison of design and measured values, energy performance indicators (EPIs), and possible optimalization.

Parameter	Design Value *	Measured Value	Optimization **	Potential CAPEX/OPEX Savings (%)	Estimated CO2 Reduction (t/Year) ***
Installed heating capacity [kW]	895 kW	158.1 kW (peak value)	189.7 kW	CAPEX: ~30–35%	-
Installed electricity capacity [kW]	348.6 kW	106.4 kW (peak value)	127.7 kW	CAPEX: ~25–30%
EPI (heat) [kWh/m2 UA]	2062.8	278.5	-	OPEX: ~20–25%	180–300
EPI (electricity) [kWh/m2 UA]	803.4	192.3	-	OPEX: ~15–20%	80–120
EPI (heat) [kWh/m2 WS]	8101.9	1093.8	-	-	-
EPI (electricity) [kWh/m2 WS]	3155.7	755.4	-	-	-

* Assume that the facility is operated for the entire year at maximum power demand as designed (8760 h). ** Assume a power reserve of 20%. *** Based on avoided energy consumption of 365–815 MWh annually and a grid emission factor of Polish power system in 2023, amounting to 0.72 tCO₂/MWh (Poland, 2023).

compares current design values with a hypothetical optimized scenario. Optimized values assume lower installed capacities that align more closely with measured peak loads and improved energy performance indicators. This comparison highlights the potential to reduce over-sizing and improve system efficiency by utilizing more accurate demand estimations during the design phase. The optimization was calculated by adopting the measured peak load values for heat and electricity and applying a 20% reserve capacity, consistent with standard engineering practice. This approach reflects a more realistic sizing strategy compared to the original design assumptions, which were based on maximum simultaneous demand (8760 h/year of operation). The comparison highlights the extent of oversizing in the design phase and provides an indication of potential efficiency gains.

The optimization scenario presented in Table 7 was developed by combining empirical data from the BMS with sensitivity analyses and performance factors. The most effective demand reductions were achieved through BMS-driven strategies, including adjustment of setpoints for ventilation airflows, pool water temperature, and occupancy schedules; analysis of operational logs to identify part-load behavior of equipment; and measures reported in the literature to improve swimming pool energy efficiency. The assumptions also considered future reduction options, such as a 1 °C decrease in pool water temperature, the use of seasonal pool covers to limit evaporation losses, an increased share of on-site PV generation, and the prioritization of CHP operation to maximize the simultaneous production of heat and electricity before engaging auxiliary boilers or grid supply. While the current study focuses on optimization derived from the comparison of measured versus design values, these additional measures could further reduce the facility’s energy performance indicators (EPIs).

4. Conclusions

This study offers a comprehensive evaluation of the energy performance of the indoor swimming pool facility in Wrocław, Poland, commissioned in 2022. A detailed analysis of operating parameters obtained from the building management system (BMS) confirmed that the actual energy consumption in properly designed water centers can be significantly lower than the values described in European design guidelines and references. The research results presented confirm the effectiveness of using an integrated heat recovery, automation, and optimization system, as well as the validity of selecting these systems at the design stage. The analysis of the research for a swimming pool facility with a usable area of 3800.84 m² and a water surface area inside the building of 467.7 m², 967.7 m², including the outdoor pool, conducted in the period from July 2022 to July 2024, showed the following:

• The maximum average heat demand during the coldest month represents 23% of the designed capacity, necessitating a thermal power output of 158 kW;
• The maximum average monthly electricity demand is 103 kW, constituting 30% of the total design capacity;
• The annual heat consumption for the heating facility was equal to 59% of the total energy required, while the annual electricity consumption was 31%;
• The cogeneration system contributed an average of 67% of the heat and 48% of the electricity supplied to the facility;
• The heat demand fulfilled by gas boilers, constituting 33% on average, amounted to 29.9 MWh;
• During the summer period, the photovoltaic panels supplied the aquapark with a peak electricity output of 13%, resulting in an average monthly supply of 7.4 MWh;
• The energy performance indicator (EPI) values obtained for the considered period are as follows: EPI_UA = 278.5 kWh/m², EPI_WS = 1093.8 kWh/m² for annual heating energy and EPI_UA = 192.3 kWh/m², EPI_WS = 755.4 kWh/m² for annual electrical energy. The coefficient relating to the average value of users, which for the analyzed Aquapark is equal to 17,756 visitors, is EPI_Vis. = 3.4 kWh/visitor (electricity demand) and EPI_Vis. = 4.9 kWh/visitor (heat demand);
• Comparison of design assumptions with measured values (Table 7) shows that installed heating capacity exceeded the actual peak demand by more than 70%, while the electrical capacity was oversized by ~65%. Correcting for this oversizing with a 20% reserve margin could reduce CAPEX by ~30–35% and OPEX by ~20–25%. This optimization corresponds to potential annual savings of 365–815 MWh, equivalent to approximately 260–590 tCO₂ avoided.
• The analysis confirms that both decentralized and well-optimized centralized systems can achieve high energy efficiency, provided that smart control, demand-driven operation, and recovery technologies are effectively integrated;
• Facilities should be meticulously designed with a comprehensive analysis of utility consumption, incorporating the use of renewable energy sources and strategies focused on reducing utility consumption and reclaiming energy;
• The design process should consider the diversification of heat sources and the optimization of efficiency in response to momentary demand, while also ensuring a balance between electricity production and supply that aligns with periodic demand;
• Energy management protocols should be universally implemented across all swimming pool facilities. This includes the establishment of energy plans to compare current energy consumption with historical billing periods and the integration of optimization strategies aimed at achieving energy savings;
• The meticulous monitoring and visualization of energy and heat consumption will facilitate the rationalization of energy management within the facility;
• The introduction of pioneering solutions tailored toward closing the circuits of media consumption within a single facility harmonizes with the fundamental precepts of sustainable development;
• This study’s contribution lies in providing high-resolution operational data for EPI benchmarking under full occupancy in a temperate climate, supporting the development of more accurate performance-based design frameworks;
• The innovation of this research lies in demonstrating, with two years of empirical data, that the real energy demand in modern aquatic facilities is significantly lower than predicted by design guidelines. By integrating multiple energy sources and advanced automation, the case study facility achieved EPI values considerably below European averages, establishing new benchmarks for efficiency in temperate climates. Moreover, a direct comparison of design versus measured demand highlights the risks of systematic oversizing and provides evidence for more precise demand-driven design approaches. These insights contribute to bridging the gap between theoretical models and real operational performance in swimming pool facilities.
• The limitations of this study include the analysis of a single facility, the absence of climate-adjusted normalization, and the exclusion of life cycle assessment (LCA) and CO₂ emission metrics. Although limited to a single facility, this study provides valuable information given the scarcity of reliable data from swimming pools. Future research, including the Sister Facility, will enable multi-case validation and strengthen the applicability of the findings.

The forthcoming plan includes continuous monitoring of media consumption and a comprehensive comparative analysis of the Polish Aquapark facility in relation to a comparable establishment utilizing heating via a heat pump and cogeneration. Additionally, the authors’ keen interest lies in structuring the operational schedule of the facility and its equipment based on energy exchange markets, while also endeavoring to accurately forecast media consumption to optimize procurement strategies.

Future research should focus on comparisons of swimming pool facilities in different climate zones, using standardized energy performance indicators (EPIs) to improve benchmarking and design practices. Additionally, the integration of life cycle assessment (LCA) and carbon footprint analysis would allow for a more comprehensive evaluation of environmental and economic performance. The influence of user behavior and usage patterns should also be examined, particularly in relation to adaptive control and automation systems. Finally, more studies should assess the retrofit potential of older facilities to verify the applicability of modern energy-saving technologies in existing infrastructure.