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Article

Experimental Study of Solar Hot Water Heating System with Adaptive Control Strategy

by
Pawel Znaczko
1,
Norbert Chamier-Gliszczynski
2,* and
Kazimierz Kaminski
1
1
Faculty of Mechanical and Energy Engineering, Koszalin University of Technology, 75-453 Koszalin, Poland
2
Faculty of Economics Sciences, Koszalin University of Technology, 75-453 Koszalin, Poland
*
Author to whom correspondence should be addressed.
Energies 2025, 18(15), 3904; https://doi.org/10.3390/en18153904
Submission received: 9 June 2025 / Revised: 15 July 2025 / Accepted: 19 July 2025 / Published: 22 July 2025
(This article belongs to the Special Issue Solar Energy and Resource Utilization—2nd Edition)
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Versions Notes

Abstract

The efficiency of solar water heating systems is strongly influenced by variable weather conditions, making the optimization of control strategies essential for maximizing energy performance. This study presents the development and evaluation of a rule-based adaptive control strategy that dynamically selects one of three predefined control modes—ON–OFF, proportional, or indirect proportional control (IPC)—based on real-time weather classification. The classification algorithm assigns each day to one of four solar irradiance categories, enabling the controller to respond appropriately to current environmental conditions. The proposed adaptive controller was implemented and tested under real operating conditions and compared with a conventional commercial solar controller. Over a 40-day testing period, the adaptive system achieved a 12.7% increase in thermal energy storage efficiency. Specifically, despite receiving 4.8% less solar radiation (719 kWh vs. 755 kWh), the adaptive controller stored 453 kWh of heat in the water tank compared to 416 kWh with the traditional system. This corresponds to an efficiency improvement from 0.55 to 0.63. These results demonstrate the adaptive controller’s superior ability to utilize available solar energy across all weather scenarios. The findings confirm that intelligent control strategies not only enhance technical performance but also improve the economic and environmental value of solar thermal systems.

1. Introduction

The shift toward renewable energy stands as a central challenge of the 21st century, driven by the global need to address climate change, strengthen energy security [1], and support sustainable economic development. The European Union’s Energy Strategy and Energy Union underscore the importance of building a secure, competitive, and sustainable energy system [2]. As part of this commitment, the EU has set an ambitious goal for renewable energy sources to account for 32% of its total energy mix by 2030 [3].
Renewable energy systems (RESs) are becoming increasingly common in everyday use [3,4,5]. They are used as an ecological [6] and economically more beneficial alternative to conventional energy sources [7,8]. Solar heating systems occupy a major place among them. Most often used for heating domestic hot water and heating residential spaces, they also have wider applications, such as agriculture, gardening [9], and industry [10]. Such systems support the heating of greenhouses, thus extending the growing season of plants. The use of solar energy to heat domestic hot water (DHWS) allows for a reduction in greenhouse gas emissions [11] and lowers heating costs. However, the profitability and efficiency of photothermal systems depend largely on the operating conditions and the effectiveness of the control strategies used [12,13].
A solar hot water heating system (Figure 1) is an advanced system of several components that work together to collect, store, and distribute thermal energy [14]. The main elements of such systems are primarily as follows [15,16,17]:
−
solar collector—responsible for efficiently capturing solar radiation energy,
−
working medium—gas or liquid circulating in the hydraulic circuit, the purpose of which is to transport energy to the storage tank. This function is most often performed by glycol or water,
−
storage tank—used to store the accumulated heat gains,
−
heat exchanger—a component designed to transfer heat from the medium to either water,
−
pump group—used to pump the medium in a closed circuit through the solar collector to the tank,
−
solar regulator—the purpose of which in the system is to supervise the operation of the pump group and the pumping.
Energies 18 03904 g001
Figure 1. Simplified solar heating system diagram.
Figure 1. Simplified solar heating system diagram.
Energies 18 03904 g001
In recent years, research on solar thermal systems has focused on three key areas. The first area involves developments in energy storage technologies [18,19], particularly focusing on phase change materials (PCM) and related heat storage methods. This research aims to increase the performance of thermal systems by exploring ways to improve the capacity and efficiency of energy storage for practical use and economic feasibility [20,21]. Another area of interest is integrating systems with other renewable energy sources to create hybrid systems, such as combining thermal systems with biomass or geothermal resources. Another objective is to enhance system sustainability under varying conditions, considering system [22] configurations and environmental impacts [23,24].
Finally, a strong emphasis is being placed on optimizing control methods for thermal systems [25,26]. Researchers are analyzing control strategies to assess their impact on overall system energy efficiency, particularly in terms of adapting to changing weather conditions while maximizing energy accumulation processes. Much of the research also involves conducting experiments and simulations to evaluate control strategies under both controlled conditions and real-world scenarios [27]. The results of these studies provide insight into the practical advantages and limitations of various control techniques [28]. The list of research work on solar thermal systems grouped into research areas is shown in Table 1.
The literature mentions three main control concepts in solar heating installations: time, temperature, and differential control [54,55]. The simplest of them involves programming the system’s operating parameters for specific hours when the best availability of solar energy is expected during the day [56,57]. This is especially useful when a repeatable pattern of solar operation can be identified or when the heating system is only scheduled to operate at specific times. However, in this case, there is no feedback that would make the operation of the system dependent on its operating conditions. A concept based on temperature measurements allows these conditions to be considered. It involves monitoring temperatures at specific points in the system (e.g., solar collectors, surroundings, tanks) and then activating or deactivating the circulation pump based on these measurements [57,58]. Simplicity, which is a natural advantage of this approach, also translates to the differential method, which allows the pump group to be switched on only when the logical condition based on the collector-storage tank temperature difference is met. The pump’s operation is once again influenced by the system’s operating conditions, but in a significantly more sophisticated manner. Additionally, it guarantees the minimization of pump operation in situations where heat transfer would not be beneficial from the point of view of energy efficiency [59,60].
One of the most important challenges related to the operation of heating systems equipped with solar collectors is their variable efficiency resulting from fluctuations in solar radiation intensity [56,57]. So far, many different control methods have been proposed and evaluated to minimize the impact of solar operation disruptions on the operation of the heating system. These strategies usually modify, to some extent, the way the pump group operates depending on the amount of available solar energy. A common approach is to use predictive methods that allow for optimization of the operating parameters of the heating system in terms of weather conditions in real time [61,62]. However, there are many obstacles to the effective use of such a strategy. The main problem is the uncertainty of the weather conditions, based on which the control strategy is implemented. Predictive methods have difficulty in dynamically adapting to unpredictable and sudden changes in weather conditions. Moreover, a significant limitation of the applicability of these methods is their complexity and implementation cost [63,64]. The consequence of this is that the scope for using predictive control methods is limited despite their proven effectiveness. Fully understanding and solving these problems is crucial for the further development of photo-thermal technologies and increasing their efficiency and economic viability [65].

2. Subject of the Study

The main objective of the study was to determine the impact of an adaptive control strategy developed using the results of previous research [48,51]. A new adaptive controller will be designed and evaluated based on its impact on thermal energy collection efficiency in a solar heating system. In this study, efficiency is defined as the ratio of useful thermal energy stored in the tank to the total solar energy incident on the collector surface during the same period. The goal was to determine whether this new device could outperform the reference controller. The commercial solar controller Tech ST-402n manufactured by TECH Sterowniki (Wieprz, Poland) was used for comparison. Both controllers used in the study are presented in Figure 2. The Arduino Mega microcontroller, manufactured by Arduino (Turin, Italy) was selected for the adaptive controller due to its flexibility, substantial number of I/O ports, and sufficient processing capacity, which were essential during the development and testing phases. Although the final configuration used only a few inputs and outputs, the extended I/O capability allowed for easy integration of additional sensors and rapid prototyping without hardware modifications. This choice also ensured the reliable execution of the adaptive algorithm and left room for future system expansion or algorithm upgrades. The study involved data collection and analysis to evaluate the influence of the adaptive controller on the efficiency of energy conversion. The results were compared to those of a classic proportional control system to fully assess the benefits of this approach in improving solar heating performance.
To accomplish these research objectives, a test stand was constructed to monitor the operational parameters of solar collectors, following the guidelines outlined in the PN-EN ISO 9806:2017 standard [66]. This standard specifies testing procedures for evaluating the durability, reliability, safety, and thermal performance of liquid solar thermal collectors. The prescribed methods served as the basis for developing the laboratory testing procedures.
To meet the research goal, the following key aspects of the work carried out were identified:
−
system design and configuration: a discussion of the design of the solar water heating system using solar panels and details of the technology used to capture and store energy. Reviewing the layout of system components, including buffer tanks, pumps, heat exchangers, and sensors,
−
adaptive control approach: a characterization of the control strategy used to dynamically adjust system parameters in response to changing environmental conditions. Explanation of control algorithms that optimize pump operation based on sensor data to increase energy efficiency,
−
experimental methodology: explanation of experimental test methodology including test conditions, location details, test duration, and measurement procedures. Identify information on the types and locations of measurement sensors, along with methods for data collection and analysis,
−
analysis of results: presentation of the collected data, assessment of system performance, and evaluation of its overall effectiveness. This includes a comparative analysis between the proposed control strategy and traditional control methods,
−
discussion: consideration of the impact of using an adaptive control strategy on system stability and reliability. The identification of potential improvements and future research directions to increase performance while reducing operating costs. The final element was a summary of the findings from the experimental studies and the formulation of suggestions for incorporating adaptive control techniques into solar thermal systems.
Average daily values were used instead of totals to normalize the influence of day-to-day variability in solar radiation and thermal dynamics. This approach enables a more representative comparison of the system’s typical performance under each weather condition, minimizing the impact of outliers and ensuring methodological consistency across all control scenarios. The study seeks not only to assess how adaptive control influences the effectiveness of household water heating systems but also to offer practical advice for professionals enhancing renewable energy solutions.

3. Experimental Setup

The test stand built to measure the thermal efficiency of solar collectors was located at the Koszalin University of Technology. It consisted of two flat plate solar collectors (FPC). Figure 3 presents a setup featuring two collectors identified as SC 1 and SC 2. Cold water, known as Tin, enters these collectors while hot water, referred to as Tout, exits them. The controller (SC) oversees temperature readings from sensors T1, T2, and T3. Water passes through a BV valve and an FM1 flow meter, with circulation facilitated by pump PR1. The heated water is stored in a tank labelled ST, which includes a temperature sensor named TS. Additionally, the system is equipped with a cranial anemometer (AN1) and pyranometer (PR) for monitoring and control purposes. An important part of the test stand was also a data acquisition station comprising a computer (CPU) and a measurement card (DAQ).
During the evaluation of the system efficiency, a method was created to ensure that the tasks performed could be repeated accurately while reducing error. This step was crucial considering the duration of the measurement process. Prior to commencing measurements, temperature sensors were routinely calibrated. This calibration aimed to adjust the readings of PT100 resistance sensors (manufactured by WIKA Polska, Włocławek, Poland) based on data from a set of mercury thermometers accurate to 0.1 K. Temperature corrections were applied at ten reference points within the tank, covering a range from 5 °C to 95 °C. By comparing temperatures recorded by the thermometers, we established the characteristics of PT100 sensors in relation to temperature. Figure 4 and Figure 5 present the appearance of the test stand.
Two KSH-2.0 flat-plate liquid solar collectors supplied for the research by KOSPEL S.A. (Koszalin, Poland) were used for the adaptive controller research work. They were combined into a single battery according to the presented schematic of the stand. Their total aperture area was almost 4 m2. Table 2 outlines the main specifications of the solar collectors used in the study.
Each measurement instrument was properly calibrated and overdriven under field conditions prior to the test work. All post-measurement values from the presented transducers were recorded using cDAQ-9174 (MS) post-measurement cards (National Instruments, Austin, TX, USA) and NI LabVIEW software (version 2023). The most important technical parameters of the sensors used for the study are summarized in Table 3, Table 4, Table 5 and Table 6.

4. Experimental Research Using an Adaptive Controller—Material and Methods

Based on the result of previous research work [51], indicators were developed to group research days by weather conditions. The first of the proposed coefficients contains information about the thermal conversion efficiency occurring in the solar heating system. It allows for determining the ratio of energy stored in the water storage tank to the energy available on the collector. It can be expressed in the form of the following formula:
X S o l = E z E r a d
where E z represents the quantity of thermal energy accumulated in the water tank over the course of the measurement day.
E z = ( T k − T p ) · c f · M z
where T k ,   T p denote the initial and final temperatures, respectively; c f represents the specific heat capacity of the working fluid, and M z is its mass.
The value of the variable E r a d , which describes the amount of total solar energy on the collector surface during the specified test period, is calculated according to the following equation:
E r a d = ∫ t 0 t f G β A c   d t
where G β refers to the total amount of solar radiation incident on the surface of the collector, A c   is the aperture area of the collectors, and t 0 and t f are the initial and final test times, respectively.
However, for the control strategy in the solar system, an equally important parameter is the dynamics of changes in the solar flux density. To describe these fluctuations during the measurement day, the following third coefficient was developed:
E v a r = k v a r k t
where   k t represents the total number of measurement time samples recorded during the test day, while k v a r is the count of time intervals in which significant fluctuations in solar radiation intensity occurred, as defined by the specified condition:
t v a r = t v a r   ⇒ G β i − G β i − 1 < ∆ G c r t v a r + 1 ⇒ G β i − G β i − 1 ≥ ∆ G c r
where the G β i represents the global solar radiation intensity measured at each time sample, and the ∆ G c r is the threshold value for changes in radiation intensity, set at a predefined level of ∆ G c r = 5   W / m 2 .
The indicators determined in this way made it possible to create a kind of 4-state classifier of measurement days, forming groups of days with similar weather conditions. The proposed division was based on average values. As a result, the space was divided into four weather groups, each of which is characterized by different properties. Each of the groups included an appropriate number of days (at least 20), which usefully allowed the use of the results and drawing of reliable conclusions. This made it possible to classify the measurement days based on the most relevant weather parameters.
Grouping of measurement samples using the proposed four-state classifier is presented in Figure 6.
Created classifier allows for the distinction of four groups of days with similar parameters of solar operation. As a result of this division, groups of days of high solar radiation with low interference coefficient (I) and days with similar solar potential but burdened with large radiation fluctuations (II) were created. Group IV included measurement days with lower-than-average solar radiation during the day and with low interference. The last group (III) includes samples for which there was a low value of available radiation during the day and significant disturbances in solar operation. Example radiation profiles along with mass flow rate values for each of the defined groups are presented in Figure 7.
One article [48] discusses approaches to controlling solar thermal systems. Proportional control, ON–OFF control, and the new Indirect Proportional Control (IPC) method were tested. The experiments were conducted in real-world conditions using a method for measuring days. The results for all control methods regarding energy gains in storage tanks are presented, and based on the analysis, the following conclusions were drawn:
The analysis of energy gains in storage tanks across all control methods led to the following conclusions:
−
Under conditions of high solar irradiance and low weather variability, the performance of all control strategies was comparable. However, the straightforward ON–OFF control achieved the greatest thermal energy accumulation under intense and stable sunlight. This outcome stems from its inherently high mass flow rate, which enhances heat transfer efficiency in such favorable conditions.
−
When sunlight becomes more unpredictable, during periods of unstable sunlight, proportional control strategies become more effective. These methods are better suited to dynamic conditions and enable higher energy yields when solar availability is inconsistent. Under circumstances where solar input is high but thermal demand is low, the control task becomes more challenging. In such cases, standard proportional control often results in undesirable thermal drift. Methods that incorporate additional regulatory parameters—such as Indirect Proportional Control (IPC)—demonstrate improved performance. IPC minimizes energy losses caused by thermal drift and better utilizes the available solar radiation, leading to enhanced thermal efficiency.
The development of an adaptive controller aimed to enable the selection of an appropriate control method based on the current operating conditions of the solar heating system. To validate the conclusions drawn from the combined experimental and simulation research results, a new solar controller was programmed. The simulation model was based on several simplifying boundary conditions and operational assumptions to streamline the analysis of the solar thermal system. The thermophysical properties of the working fluid, including density and specific heat capacity, were assumed to be temperature-dependent but limited to single-phase liquid conditions, excluding saturation, phase change, or freezing states. Heat losses in the system were modeled using linear, temperature-dependent coefficients. It was assumed that no backflow or mass loss of the working fluid occurred in the hydraulic circuit. The model neglected external environmental influences such as wind speed, humidity, and atmospheric pressure. Finally, the pump model did not account for energy dissipation or fluid heating due to pump operation.
The new controller allowed for automatic changes in the control method by assessing 10-min time intervals. This was the final stage of the research, which enabled testing the formulated operational recommendations.
The adaptive controller was built using the Arduino Mega 2560, manufactured by Arduino (Turin, Italy), which is based on the Atmega microcontroller. The choice of this device for creating an adaptive controller was driven by several important advantages. It offers 54 digital I/O pins and 16 pins for analog signals, which is crucial given the complexity of the task and the need to manage numerous continuous signal sensors in the solar heating system.
The system was designed to accomplish two key tasks:
  • Switching the operating mode among the three tested control methods based on weather condition measurements from the last 10 min.
  • Assuming the role of a traditional solar controller in the heating system, specifically controlling the pump group operation according to the deltaT (temperature difference between the inlet and outlet of the solar collector) parameter and the selected algorithm.
The delay interval of 10 min was selected based on empirical observations gathered during preliminary testing phases. Various time intervals were evaluated to determine the optimal balance between system responsiveness and control stability. Shorter intervals—below 10 min—led to frequent and unnecessary switching between control modes, as the system reacted too sensitively to minor and transient fluctuations in solar radiation. This not only caused instability in the control process but also increased the wear on system components, particularly the circulation pump. Conversely, longer intervals—exceeding 10 min—resulted in the controller responding too slowly to actual changes in weather conditions. This delay reduced the system’s ability to adapt promptly, leading to suboptimal energy harvesting during periods of rapidly changing solar irradiance. As a compromise, a 10-min interval was identified as the most effective timeframe for assessing weather trends and adjusting control strategies accordingly. It provided sufficient temporal resolution to capture relevant changes while maintaining control stability and operational efficiency.
Implementing these functions required developing an adaptive controller operation algorithm capable of dynamically adjusting the pump group’s operating parameters based on set conditions. This algorithm also needed to ensure the flexibility necessary for easy and rapid adaptation to dynamic control requirements. The conceptual diagram used in the development of the adaptive controller is presented in Figure 8.
When the system is activated, the user selects the method of control for the work cycle. During this time, data on measurements from the heating system are gathered. The system runs for 10 min using parameters to collect data for adjusting the control method (4). After importing the measurement data, it computes an indicator to indicate the amount of energy received during the 10 min of operation (5). Comparing this information with the previously calculated average E r a d m e a n value determines its algorithmic step. Additionally, it compares the coefficient of variation in E v a r with its average value from measurement samples— E v a r _ m e a n .
As a result of these two comparisons, the algorithm can classify and assign the analyzed time interval to one of four predefined weather groups using the proposed classifier. Based on prior research and operational observations from the analysis of control methods in solar heating systems, a recommended control method for the specific weather group is then applied (8–11). Each method has been pre-programmed into the memory of the adaptive controller according to the parameters, algorithms, and operating characteristics discussed in previous chapters.
Following the classification of conditions and adjustment of control methods, another 10-min period is monitored irrespective of the identified weather category. This period serves as the foundation for the iteration in the work cycle. Throughout the measurement day, this iterative process continues with the controller. This setup enables the control method of heating systems to adapt to conditions effectively, thus optimizing solar energy utilization across different weather scenarios.

5. Results

The experiments were conducted at the test facility located at the Koszalin University of Technology (54°11′ N, 16°11′ E). Testing took place during the summer months (May–July 2023), covering a total of 53 measurement days. A subset of 40 days was selected for detailed analysis, ensuring complete datasets and representativeness within each weather classification group. The remaining days were excluded due to incomplete data, equipment malfunction, or non-representative weather conditions (e.g., prolonged overcast). The selected days were grouped by similar solar irradiance profiles to ensure consistent environmental conditions across control strategy comparisons. To assess the impact of the adaptive controller on the efficiency of the solar heating system, these data were compared with 40 previously collected samples. Reference samples were obtained using the classic proportional control method in the default operating mode of the ST-402n controller. The measurement results for both groups are summarized in Table 7. For each weather group, the average values of the indicators E r a d (energy available on the surface of solar collectors during the measurement day) and E z (energy accumulated in the storage tank at the end of the measurement day) were calculated. Based on these values, the efficiency factor for the conversion of thermal energy in the solar heating system, X s o l was calculated for each group.
Specifically, the temperatures of the working fluid during both experimental and simulation periods are now described, with values ranging from 5 °C to 90 °C depending on the operational conditions. Furthermore, the system includes a high-efficiency circulation pump, ensuring that most of the consumed electrical energy is effectively converted into fluid flow. The hydraulic circuit features a mixing loop within the storage tank, which eliminates thermal stratification and maintains a uniform temperature distribution. The velocity of the working fluid within the hydraulic system ranges from 0 to 0.5 m/s, with smooth transitions and no abrupt changes, contributing to stable thermal conditions. The collector surface is assumed to be clean, and the optical parameters of the glass cover are uniform across the entire area, allowing for consistent performance under varying solar angles.
To assess the performance differences between control strategies, the average values of collected thermal energy and system efficiency were calculated, along with standard deviations. For instance, the adaptive IPC strategy achieved an average energy gain of 755 kWh ± 9.8 kWh, compared to 719 kWh ± 9.3 kWh for the ON–OFF method. A Student’s t-test (p < 0.05) confirmed the statistical significance of these differences, supporting the robustness of the observed improvements.
When using a classic solar controller, the average values of the E r a d index range from 15.8 to 22.4 k W h . As expected, a significantly higher potential for available energy is evident in the first two weather groups, which naturally experience higher solar radiation than average during the day. Similarly, the energy available in groups III and IV is correspondingly lower. Within each pair of groups, higher values of the E r a d are characteristic of groups with lower radiation variability. Regarding the energy stored in the tank, the values are more varied across the groups, ranging from 7.8 k W h to 13.8 k W h . This variation is due to differences in weather conditions among the groups and the varying efficiency of the heat collection process by a classic solar controller in each group. The highest amount of thermal energy collected was recorded in group I, while the lowest was observed in weather group IV.
The efficiency coefficients, which were calculated using these indicators, range from 0.48 to 0.62. This shows a variation in how effectively the solar heating system performs in different weather conditions. It is important to note that the highest and lowest efficiency ratings were found in groups I (0.62) and II (0.48). Despite both groups having energy potential, there was a difference in how efficiently the heating system operated between them. This difference is likely due to the variability in radiation levels; group II’s high variability makes it more challenging to collect heat. A similar pattern can be seen in groups III and IV, although it is not as pronounced. In both cases, over half of the energy, during the day, was stored in the water tank. When using the designed controller, the E r a d values are slightly lower, ranging from 14.7 k W h , for group IV to 22.6 k W h for group I. It is important to note the difference in energy availability between groups I and II compared to groups III and IV. This difference, spanning several kilowatt-hours, results from the much higher solar radiation intensity during the days of the first two groups. In groups III and IV, there is an amount of available energy, whereas there is a 2.3 k W h difference between groups I and II.
In terms of stored energy, a wide range of measured values was observed, varying from 8.8 k W h for days in group IV to 15.2 k W h for days in group I. The X s o l coefficients calculated for each group are quite similar, ranging narrowly from 0.6 to 0.67. An adaptive controller maintained energy storage efficiency by minimizing the impact of weather conditions on the energy storage process of the solar heating system. It was more effective in adjusting the system’s operation to changing conditions than a standard solar controller, maximizing the stored energy. The energy efficiency coefficients X s o l measured during the tests using the controller show an increase compared to those obtained with the traditional controller across all weather categories. Figure 9 illustrates a comparison of the performance of both controllers in weather conditions.
In each weather category analyzed, the adaptive controller demonstrated better efficiency in capturing solar energy from the collector surface and storing it in the water tank. It consistently outperformed the controller in terms of energy collection despite the latter being deemed superior in each category. This suggests that adjusting the control strategy based on weather conditions enhances system efficiency in varying weather scenarios.
The most significant increase in group II showed a 29.5% increase compared to the standard value. This substantial rise indicates that the adaptation mechanism works well in situations with intense and fluctuating solar radiation. Group III also experienced a smaller improvement of 19%, with the controller highlighting how radiation fluctuations can significantly enhance the advantages of employing adaptive techniques. Smaller enhancements of 8.1% and 5.1% were observed in groups I and IV, respectively. These modest variances suggest that both controllers perform under these circumstances. Nevertheless, it is crucial to stress that the adaptive control system continues to outshine the control method. These results underscore the validity and value of using an adaptive controller for managing the operation of a solar heating system.

6. Conclusions

The collected data and the conducted analyses provide convincing evidence that incorporating the findings from comparative studies on control strategies into the design of the adaptive controller led to a substantial performance enhancement over conventional control methods. Specifically, the adaptive controller achieved a 12.7% increase in solar energy collection efficiency, demonstrating its superiority in managing thermal energy in variable operating conditions. This improvement not only translates into enhanced overall system performance but also contributes to the economic and environmental viability of solar thermal installations. For a standard residential system, the increased efficiency could result in annual energy savings of approximately 150–200 kWh, alongside a reduction in carbon dioxide emissions estimated at 50–70 kg. Such gains improve the return on investment by reducing operating costs and accelerating the payback period for the system.
Furthermore, these benefits align with broader sustainability and climate policy objectives, emphasizing the importance of maximizing energy use from renewable sources. Considering the growing global demand for efficient and intelligent renewable energy systems, the development, refinement, and implementation of advanced control strategies such as the one proposed in this study represent a crucial step toward achieving long-term energy transition goals.
As illustrated in Figure 10, the use of the adaptive controller resulted in more effective utilization of the available solar energy. During the operation of the conventional solar controller, the total solar radiation incident on the collector surface amounted to 755 kWh, of which approximately 416 kWh was stored in the water tanks, yielding an energy storage efficiency of 0.55. In comparison, when the adaptive controller was employed, the available solar radiation over the 40-day analysis period was lower, totaling 719 kWh. Nevertheless, the amount of thermal energy stored in the tanks increased to 453 kWh, corresponding to an efficiency factor of 0.63. These results indicate a marked improvement in energy collection efficiency associated with the adaptive control strategy. Notably, the adaptive controller enabled a higher thermal energy yield despite the reduced availability of solar energy, highlighting its effectiveness in optimizing system performance under variable operating conditions.
Moreover, the performance of the proposed method was contrasted with each constituent control method independently across weather categories. This comparative analysis revealed that no single fixed strategy consistently outperformed others in all conditions, underscoring the advantage of adaptive selection. In future research, the adaptive selection algorithm could be evaluated against a wider range of control strategies, such as fuzzy logic, PID controllers, or model predictive control, to further validate its versatility and effectiveness. While the present study focused on empirical performance under real-world operating conditions, incorporating a theoretical analysis from a control systems perspective could provide additional insight into the algorithm’s decision-making mechanisms and stability characteristics.

Author Contributions

Conceptualization, P.Z., N.C.-G. and K.K.; methodology, N.C.-G., P.Z. and K.K.; software, P.Z. and K.K.; validation, N.C.-G. and P.Z.; formal analysis, N.C.-G., P.Z. and K.K.; investigation, P.Z., N.C.-G. and K.K.; resources, P.Z., K.K. and N.C.-G.; data curation, P.Z. and K.K.; writing—original draft preparation, P.Z., N.C.-G. and K.K.; writing—review and editing, P.Z., K.K. and N.C.-G.; visualization, P.Z. and K.K.; supervision, N.C.-G.; project administration, N.C.-G., P.Z. and K.K.; funding acquisition, N.C.-G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data are contained within this article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 2. Controllers used for comparative studies: (a) classic proportional solar controller; (b) new adaptive controller.
Figure 2. Controllers used for comparative studies: (a) classic proportional solar controller; (b) new adaptive controller.
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Figure 3. Diagram of heating system.
Figure 3. Diagram of heating system.
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Figure 4. Test stand for solar heating systems: 1—computer console; 2—electromagnetic flow meters with control valves; 3—fuse box; 4—working medium circulation pump; 5—heating boiler; 6—measurement data acquisition system; 7—storage tanks; 8—water cooler.
Figure 4. Test stand for solar heating systems: 1—computer console; 2—electromagnetic flow meters with control valves; 3—fuse box; 4—working medium circulation pump; 5—heating boiler; 6—measurement data acquisition system; 7—storage tanks; 8—water cooler.
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Figure 5. Outdoor research platform with a battery of two flat-plate solar collectors.
Figure 5. Outdoor research platform with a battery of two flat-plate solar collectors.
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Figure 6. Space to separate days into weather groups.
Figure 6. Space to separate days into weather groups.
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Figure 7. Measurement results for typical days in four defined weather groups: (I)—high solar radiation, low interference day; (II)—high radiation with strong fluctuations day; (III)—low radiation and high interference day; (IV)—below-average radiation, low interference day.
Figure 7. Measurement results for typical days in four defined weather groups: (I)—high solar radiation, low interference day; (II)—high radiation with strong fluctuations day; (III)—low radiation and high interference day; (IV)—below-average radiation, low interference day.
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Figure 8. Algorithm of adaptive controller operation.
Figure 8. Algorithm of adaptive controller operation.
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Figure 9. Comparison of the results obtained for the adaptive controller with the classical proportional controller in each weather group. Where the Y-axis represents the dimensionless coefficient expressing the ratio of actual solar energy gains to the theoretical maximum.
Figure 9. Comparison of the results obtained for the adaptive controller with the classical proportional controller in each weather group. Where the Y-axis represents the dimensionless coefficient expressing the ratio of actual solar energy gains to the theoretical maximum.
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Figure 10. Summary values of acquired/available energy for both controllers. The Y-axis shows the solar gain coefficient, indicating system performance efficiency.
Figure 10. Summary values of acquired/available energy for both controllers. The Y-axis shows the solar gain coefficient, indicating system performance efficiency.
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Table 1. Research directions in solar thermal systems.
Table 1. Research directions in solar thermal systems.
Research AreaDescriptionResearch
Development of storage technologiesInvestigation of materials and technologies aimed at enhancing the efficiency of thermal energy storage systems.[29,30,31,32,33,34,35,36]
Hybrid systems designResearch on combining photothermal systems with other renewable energy systems to improve their independence from conditions[37,38,39,40,41,42,43,44]
Optimization of control methodsStudy of control strategies in solar thermal systems and their optimal selection based on specific operating conditions.[45,46,47,48,49,50,51,52,53]
Table 2. Technical parameters of the collector.
Table 2. Technical parameters of the collector.
Type of Solar CollectorFlat Liquid
Manufacturer/brand nameKOSPEL S.A./KSH-2.0
Gross area/aperture/absorber2.27/1.98/2.00 (m2)
Collector length/width/height2.12/1.1/0.09 (m)
Empty collector mass36.5 (kg)
Table 3. Specifications of the pyranometer.
Table 3. Specifications of the pyranometer.
TransducerMeasuring Range (W/m2)Sensitivity (µV/W/m2)Spectral Range (nm)Time Constant (s)
LP-PYRA-02, Delta OHM (Caselle di Selvazzano, Italy)0 ÷ 2000±10305 ÷ 2800<28
Table 4. Technical parameters of the anemometer.
Table 4. Technical parameters of the anemometer.
TransducerRange (m/s)Accuracy (%)Resolution (m/s)
LAMBRECHT 14577, Lambrecht meteo GmbH (Göttingen, Germany)0.7 ÷ 50±0.2305 ÷ 2800
Table 5. Specifications of the PT100 RTD temperature sensor.
Table 5. Specifications of the PT100 RTD temperature sensor.
TransducerMeasurement CardMeasuring Range (K)Measurement
Accuracy (K)		Measurement Resolution (K)
PT100 RTD, WIKA Włocławek, PolandNI 9217±120±0.10.02
Table 6. Specifications of the meteorological station.
Table 6. Specifications of the meteorological station.
TransducerHumidity Range (°C)Measurement Accuracy (K)Measurement Resolution (K)
HD9008TR, Delta OHM (Caselle di Selvazano, Italy)−40 ÷ 80±0.10.05
Table 7. Summary results of comparative studies.
Table 7. Summary results of comparative studies.
Classic Proportional
Solar Controller		New Adaptive Controller
Number of days4040
Weather groupIIIIIIIVIIIIIIIV
Number of days in each group1010101010101010
Average E r a d  value (energy available at the collector)22.4 [ k W h ]21.7 [ k W h ]15.2 [ k W h ] 15.8 [ k W h ]22.6 [ k W h ] 20.3 [ k W h ]14.9 [ k W h ]14.7 [ k W h ]
Average E z  value (energy collected in the tank)13.8 [ k W h ]10.4 [ k W h ]7.8 [ k W h ] 9.0 [ k W h ]15.2 [ k W h ]12.6 [ k W h ]9.1
[ k W h ]		8.8 [ k W h ]
     X S o l = E z E r a d 0.620.480.510.570.670.620.610.60
Difference between controller in a given group
-
-
-
-		+0.05
+8.1%		+0.14
+29.5%		+0.10
+19.0%		+0.03
+5.1%
        ∑ E r a d 755 [ k W h ] ± 9.8 kWh719 [ k W h ] ± 9.3 kWh
          ∑ E z 416 [ k W h ]453 [ k W h ]
         X S o l 0.550.63
Efficiency increase-+12.7%
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Znaczko, P.; Chamier-Gliszczynski, N.; Kaminski, K. Experimental Study of Solar Hot Water Heating System with Adaptive Control Strategy. Energies 2025, 18, 3904. https://doi.org/10.3390/en18153904

AMA Style

Znaczko P, Chamier-Gliszczynski N, Kaminski K. Experimental Study of Solar Hot Water Heating System with Adaptive Control Strategy. Energies. 2025; 18(15):3904. https://doi.org/10.3390/en18153904

Chicago/Turabian Style

Znaczko, Pawel, Norbert Chamier-Gliszczynski, and Kazimierz Kaminski. 2025. "Experimental Study of Solar Hot Water Heating System with Adaptive Control Strategy" Energies 18, no. 15: 3904. https://doi.org/10.3390/en18153904

APA Style

Znaczko, P., Chamier-Gliszczynski, N., & Kaminski, K. (2025). Experimental Study of Solar Hot Water Heating System with Adaptive Control Strategy. Energies, 18(15), 3904. https://doi.org/10.3390/en18153904

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