cooling system in residential buildings

Applied Energy 254 (2019) 113711

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Improved energy management technique in pipe-embedded wall heating/ cooling system in residential buildings

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M. Krzaczek , J. Florczuk, J. Tejchman Gdansk University of Technology, Faculty of Civil and Environmental Engineering, 80-233 Gdańsk, Narutowicza 11/12, Poland

H I GH L IG H T S

innovative energy management technique in buildings was presented. • The technique has a few clear advantages over the existing ones. • This field measurements were performed in the residential house. • Comprehensive • The measurement results confirmed the efficacy of the new technique.

A R T I C LE I N FO

A B S T R A C T

Keywords: Residential building Energy management technique Thermal Barrier Field measurements Pipe-embedded structure Fuzzy logic

Effective and environmentally responsive techniques of energy management in residential buildings are desirable for the resulting reduction of energy costs and consumption. In this paper, an improved and efficient technique of energy management in pipe-embedded wall heating/cooling systems, called the Thermal Barrier, is described. Specifically, the Thermal Barrier is a technique focused on the management and control of heat supply into and heat extraction from external walls containing embedded pipes. The installed pipe-embedded wall heating/cooling system is fully controlled by a special fuzzy logic program that synchronizes the heat supply/ extraction with variable heat loads. The main operation rule of the Thermal Barrier is to keep changes of the wall internal energy close to zero for the given reference temperature of a pseudo-surface created by an embedded pipe system of the wall heat exchanger. Comprehensive field measurement results associated with an example Thermal Barrier System installed in a residential two-story house are presented. These measurements confirmed the high-efficiency of the Thermal Barrier and its ability to use low-grade heat sources and sinks to effectively control an indoor climate. The supply water temperature was very low (25.3 °C) in the winter and very high (20.5 °C) in the summer. Daily variations of the indoor air temperature did not exceed 0.8 °C throughout the year. During the summer, the Thermal Barrier System operated in cooling-mode only from a low-grade renewable heat sink. The flexibility of the Thermal Barrier also allows for using heat sources/sinks different from those in the test house.

1. Introduction Ongoing efforts aim to reduce commercial and residential building energy consumption since recent studies show that these structures consume 40% of the total energy manufactured [1]. There are many reasons for the increased energy demand of these buildings [2]. A reduction of heat losses through a building’s opaque is still one of the most common methods used to decrease energy consumption, particularly in cold climate zones. This trend is bolstered by national and international regulations and industry standards. At the same time, climate changes reduce heating demand in cold regions and increase

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cooling demand in hot regions. While a reduction of heat losses through a building’s opaque decreases the building’s heat demand, it also introduces the risk of indoor overheating resulting in the magnification of cooling demand. Thus, a cooling system must be applied to support the thermal comfort in buildings (even in winter). For example, solar radiation is one of the most intense heat loads influencing the instantaneous heat balance in residential buildings. In well insulated buildings, a large amount of solar energy transferred through windows to indoor zones disturbs indoor thermal conditions. This feedback results in an increased of supply of cooling or heating to the building which corresponds to an undesirable growth of total energy

Corresponding author. E-mail addresses: [email protected] (M. Krzaczek), jaroslaw.fl[email protected] (J. Florczuk), [email protected] (J. Tejchman).

https://doi.org/10.1016/j.apenergy.2019.113711 Received 19 January 2019; Received in revised form 3 August 2019; Accepted 5 August 2019 0306-2619/ © 2019 Elsevier Ltd. All rights reserved.

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solar energy flux (short-wave radiation) transferred into the wall through the external surface ̇ Qspl heat supplied or extracted from 1 m2 of wall in time unit ̇ heat supplied/extracted to/from wall by heating/cooling Qsup system QT,int total change of internal energy RLTZ (aLTZ, Tsr) relationship between active flow areas of valves in LTZ and water supply temperature RLTZ (aHTZ, Tsr) relationship between active flow areas of valves in HTZ and water supply temperature Rm (am, vsr) relationship between active flow area of main valve and supply water velocity in wall heat exchangers SmI partial contribution of working mode I SmII partial contribution of working mode II SmIII partial contribution of working mode III Sv Fuzzy Mixed Variable t time T temperature Tavg volume average temperature of external wall (of all layers) Tb temperature of TB surface Tb,ref reference temperature (set-point) of TB-surface Te ambient air temperature THTZ water temperature of hot water flowing from HTZ zone to mixing device Ti indoor air temperature water temperature at the inlet of the wall heat exchanger Tin Ti,ref set-point of indoor air temperature TLTZ water temperature of cold water flowing from LTZ zone to mixing device Tse temperature of external wall surface Tsol sol-air temperature, Tsi (t ) temperature of internal wall surface Tspred predicted water supply temperature Tsr supply water temperature in mixing device outlet ws wind speed supply water velocity vsr V volume x, y coordinates density ρ average density ρavg

̇ Qrad

Nomenclature Abbreviations EFMGS EM FMGS HTZ LTZ MPC MTZ RM TABS TB TBS TM

Extended Fuzzy Mixing Gain-Scheduling Expert Module Fuzzy-Mixing Gain-Scheduling High-Temperature Zone Low-Temperature Zone Model Predictive Control Medium-Temperature Zone Reset Module Thermally Activated Building Structure Thermal Barrier Thermal Barrier System Tuning Module

Symbols

cp cp, avg fsi (t ) he, conv (t ) h1 h2 Irad l m mcor N qsup ̇ Qce Qci̇ Qi̇ Qint Qė

Qṙ

specific heat at constant pressure volume average specific heat at constant pressure measured temperature of internal wall surface convective part of heat transfer coefficient system sampling time sampling interval for reset point reset measured solar net radiation (by sensor) cubic root of test house volume mass flow rate corrected mass flow rate step number in reset set-point loop heat supplied/extracted by wall heat exchanger per cubic meter heat flux on external wall surface (convective part) heat flux on internal wall surface heat flux transferred by convection and long-wave radiation into/from indoor air through internal surface internal energy (accumulated heat) sum of heat fluxes transferred into/from wall through external surface by convection and long-wave radiation radiative net heat flux (short-wave radiation minus longwave radiation, calculated from Irad)

of TABSs must accommodate the dynamic behaviour of structural components. Hence, the computationally expensive dynamic computer simulations of buildings with those systems must be performed leading to long design phases [14]. Zhu et al. [15] proposed an advanced model (a semi-dynamic simplified model of an active pipe that was embedded in the building envelope) that was easy to use and experimentally validated. The frequency-domain finite-difference (FDFD) model was suggested by Xie et al. [16] to predict a thermal response frequency of the active pipe-embedded building envelope. A stable control system is integral to the successful operation of TABSs. One method aimed at reducing the negative effect of the thermal inertia on the TABs system controllability, is to embed the pipes near the internal wall surface in contact with the indoor air. As a result, this pipe placement improves controllability but also reduces access to accumulated energy. Romaní et al. [17] identified some control method limitations: individual room control is impossible in TABSs with high thermal mass, the topology of TABSs affects the building control and synchronization between TABS, and ventilation must be accounted for in the control design. The most basic control method of TABS is the ON/OFF control strategy typically used to control radiant floors or ceiling panels, however, the method is not optimized for energy consumption [18] and is limited for controlling TABSs embedded in light building structures. The ON/OFF control strategy

consumption. A promising solution to the problem of excessive building energy demands is the use of heating/cooling systems capable of exploiting low-grade heat sources and sinks. Many studies focus on the applications and performance of active structures with embedded pipes (heat exchangers), e.g. floor/ceiling and walls [4]. Although the literature has various names for these structures, this paper refers to them as thermally activated building component systems (TABSs) [3]. These structural techniques of low-grade energy source extraction/rejection enable the exploitation of renewable heat sources directly or through long and short-term heat source/sink systems. Most studies focused on structures with embedded pipes in floors, ceilings, and foundations [3,5–9]. Meanwhile, the pipe systems embedded in walls have become the subject of intense investigations. Li A. et al. [10] proposed a new low-grade energy system in the form of a wall with embedded pipes integrated with a ground source-coupled heat exchanger. Other researchers also indicated a great energy-saving potential of pipe-embedded walls [11–13]. Shen and Li [13] reported that walls embedded with pipes reduce heating cost by 40% and the payback period can be reduced to two years. While heating and cooling buildings with TABSs are efficient and beneficial, the design and operation of buildings and systems with such a technology pose a great challenge due to the need for advanced modelling and control methods. For example, the design 2

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office buildings since it cooperates with the existing Building Energy Management System. In CFMPC, the building is represented by its fuzzy model (each building zone is controlled by one FMPC controller and the global MPC). The CFMPC does not replace the conventional building control but it can be seen as an additional layer in a supervisory function. The resulting control system is very complex and has some disadvantages: it requires a building model that has to be implemented in a development stage and it requires high computation effort due to a parameter optimization process. The existing control and energy management methods have still several shortcomings:

relies on the control of one variable (water temperature or water velocity). The Unknown-But-Bounded (UBB) method proposed by Gwerder et al. [19] is based on controlling the supply water temperature with respect to the outdoor air temperature. For this method, the supply water temperature is shifted according to heating/cooling curves to compensate for heat losses and gains through the façade. However, the heating/cooling curves are only determined during the design stage, thus, UBB is not able to precisely control the supply water temperature. Gwerder et al. [20] developed the Pulse Width Modulation (PWM) method (discontinuous operation control method that operates on cyclic activation periods). The method uses UBB to control the supply temperature, while the flow rate of the supply water is constant and only activation periods change. Tang et al. [21] proposed a pulsed flow control method (PFM) that operates on variable activation periods. Under PFM, the on-time duration of the valve is short and fixed while the off-time varies. The authors evaluated PFM performance experimentally and reported that PFM requires a lower average water flow rate (24%) compared to the widely used variable temperature control method. In PFM, the flow rate of the supply water is constant and only activation periods change. Sui et al. [22] studied the optimization of intermittent operation control schemes of pipe-embedded envelope cooling systems and concluded that the required total cooling capacity increased with growing cooling time. They reported that the reduction of the cooling time might decrease the system energy consumption. However, the increased cooling time decreased the peak load. The further improvement of the control efficiency was achieved by implementing the Model Predictive Control (MPC) method [23] and adaptive and predictive control methods [24]. Schmelas et al. [25] employed an adaptive and predictive algorithm to control TABS. The control method uses weather data from the previous 15 days and weather forecasts for the following 24 h in conjunction with the RC network model of TABS to calculate the amount of heat to be supplied/ extracted from the building. However, since the TABS system topology may be different for different buildings, it requires a TABS model rebuild. In addition, entering the weather forecast data for the next 24 h into the control system may be difficult or impossible due to technical reasons. Additionally, the forecast may be incorrect. Viot et al. [26] presented an another approach to obtain a custom model designed for implementation in the MPC controller of long time response floor heating systems. They selected a hybrid model called ‘grey-box’ corresponding to semi-physical modelling. Measurements in the real building were used to calibrate the model and reduce the number of model parameters. The final developed model was implemented in MPC and tested in a real building [27]. Authors compared the MPC system with two other control systems, concluding that the predictive controller demonstrated the best performance. Another approach to obtaining an appropriate model for implementation in MPC controllers was presented by Prívar et al. [28]. They proposed a method of building model identification that was a combination of minimization of multistep-ahead prediction errors and partial least squares. The authors reported that applying the MPC for control of the HVAC system of the Czech Technical University building improved the energy consumption during heating seasons by approximately 20%. Hu et al. [29] applied the MPC method to floor heating systems to reduce operating costs considering all the influential variables. They reported that compared with the conventional on-off control, the MPC controller was able to implement automatic and optimal preheating. Pang et al. [30] verified the correct operation of an open source MPC toolchain developed for radiant slab systems and demonstrated its efficacy in a test facility. They reported that during the eight-day experimental period, the MPC reduced chilled water pumping energy by 42.6% and 16% cooling thermal energy savings when compared to the conventional control of the radiant slab system. However, parallel processing is required for the small computation time. Killian et al. [31] used fuzzy logic to improve the efficiency of the MPC control method. They proposed a cooperative Fuzzy MPC (CFMPC) control method that is suitable for all modern

– individual room control is not possible or limited (all methods), – the controlled variable is usually the water supply temperature or velocity (all methods), – identification and development of a model for each building (adaptive and predictive methods, e.g. MPC) – modelling of the TABS system topology for each building (adaptive and predictive methods), – the high computational cost and complexity with respect to feedback controls in conventional systems (adaptive and predictive methods, e.g. MPC), – prediction based on forecasts (adaptive and predictive methods, e.g. MPC). In the current paper, the results of custom field investigations on an improved energy management technique in a pipe-embedded wall heating/cooling system, called the Thermal Barrier (TB), are presented. TB is a technique that manages the rejection of heat into and extraction of heat out of external building walls with embedded pipes (heat exchangers) using a software control system based on an improved Extended Fuzzy-Mixing Gain-Scheduling control method. The TB concept is based on an optimal synchronization of the heat rejection (or extraction) with internal energy variations in external walls, which is achieved by maintaining the constant temperature of a pseudo-surface created by wall heat exchangers. Therefore, the control method is an inseparable ingredient of TB. The field measurements of the demonstration TB were collected from a residential two-family detached house in Warzno (northern Poland) for a duration of 17 months. Although the basics of the TB concept were detailed in [32], some significant changes to this concept were implemented in the original Fuzzy-Mixing GainScheduling control method. In summary, we presented in the paper both the Extended FuzzyMixing Gain-Scheduling control method of pipe-embedded wall heating/cooling systems which was improved as compared to the existing energy management techniques and the comprehensive long-time measurement results of heating/cooling in a residential building (with TB) that are not available in the literature. TB has a few clear advantages over the other energy management techniques: (1) it maximizes the use of heat stored in massive structural layers of external walls by synchronization of the rejection and extraction strategy with variable heat loads, (2) it rejects or extracts the exact energy amount by controlling the water velocity and water temperature at each time step, (3) it manages the magnitude and direction of the heat flux on the inner surface of external walls by keeping the constant temperature of a pseudo-surface created by wall heat exchangers, (4) it allows for individual room control, (5) it does not use weather forecast data and (6) it does not require high computation effort and can be easily implemented in relatively simple electronic devices, (7) it does not need a building model and (8) it does not need a TABS topology model. The outline of the current paper is the following. The concept of the present TB is briefly described in Section 2. The methodology of experimental investigations in the test house is presented in Section 3. The installed systems in the test house are depicted in Section 4, while the measurement set-up is discussed in Section 5. Section 6 shows the measurements results of the TB and control system performance and the 3

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symmetry axes of the pipe systems forming the wall heat exchangers and Tb is its area-averaged temperature. The set-point of the temperature Tb should be designated to keep the indoor air temperature close to the comfort temperature. The main algorithm of the control system [33] controls the temperature Tb while the outer reset set-point loop adjusts its set-point to maintain the indoor air temperature close to the preferable thermal comfort level. In TB, the external wall is treated as temporary heat storage, which is generally supplied by solar energy, heat transferred from or to the indoor air, or heat transferred from/to the ambient air and heating/cooling system. With an objective focused on balancing the physical system, TB determines a target heat quantity that should be supplied or extracted at each time step to external walls to minimize the internal energy variations. Thus, the heat supply strategy is synchronized with internal energy variations. The synchronization results in maintaining the internal energy at the level required for the constant indoor air temperature. It means that TB heats or cools the indoor space at each time step (60 s) throughout the year.

ability of TB to keep up the preferable thermal comfort level in winter and summer seasons. Conclusions are offered in Section 7. 2. Concept of improved energy management technique Thermal Barrier manages the heat rejection into or heat extraction from external walls with a wall embedded pipe system. However, its operation mode is different from existing TABS. The heat exchangers (so-called the wall heat exchangers) are situated, namely, in external walls between a thermal insulation layer and load bearing layer (if any exist), away from the wall internal surface (Fig. 1a). They are in thick layers of the high thermal capacitance material, e.g. concrete or mortar. Assuming that the heat in solids is transferred under conditions of constant pressure and volume, the internal energy may be expressed in terms of measurable temperatures. The general operating rule of TB is to maintain a constant temperature Tb of a pseudo-surface (Fig. 1b), the so-called TB-surface. The TB surface is the surface created by the

Fig. 1. Wall heat exchanger embedded in external wall: a) pipe system of wall heat exchanger and b) pseudo-surface created by pipe system of wall heat exchanger. 4

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losses in the distribution system) equal to Tsr (the water temperature in the mixing device outlet). To control vsr and Tsr at each time step and to keep variations of the internal energy close to zero (Eq. (2)), the improved Fuzzy Mixing GainScheduling (FMGS) control technique, so-called Extended Fuzzy Mixing Gain-Scheduling (EFMGS), was applied. Unlike the conventional gain scheduling control methods, the original FMGS method uses fuzzy logic to dynamically interpolate controller parameters near region boundaries based on the known local controller parameters. The FMGS approach is based on the idea of fuzzy mixing/weighting the local/modal values of each computed (automatically designed) controller parameter and can be expressed in the following form of the Principal Fuzzy Mixing Equation (PFME):

In order to explain the TB operation rule, the horizontal cross-section of the external wall may solely be considered. By simplifying a heat transfer process, the 1D system of the one-layer wall is assumed. Hence, the heat is transferred along the normal to the wall internal surface. The heat balance is expressed then as:

cp, avg ρavg V

dTavg dt

̇ (t ) + Qė (t ) + Qsup ̇ (t ), = Qi̇ (t ) + Qrad

(1)

where Tavg - the volume averaged temperature of the external wall (of all layers) (°C), cp, avg - the volume averaged specific heat at constant pressure (J/(kg·K)), ρavg - the material volume averaged density (kg/ m3), V - the volume (m3), Qi̇ (t ) - the heat flux transferred by convection and long-wave radiation into/from the indoor air through the internal ̇ (t ) - the solar energy flux (short-wave radiation) surface (W), Qrad transferred into the wall through the external surface (W), Qė (t ) is the sum of heat fluxes transferred into/from the wall through the external ̇ (t ) - the heat surface by convection and long-wave radiation (W), Qsup supplied/extracted to/from the wall by the heating/cooling system (W). The TB operation rule is to keep variations of the accumulated heat close to zero for the given reference temperature (set-point) Tb,ref of the TB-surface until the set-point Tb,ref is changed:

cp, avg ρavg V

dTavg dt

≈ 0.

Sv = SmI α (Tsol ) + SmII β (Tsol ) + SmIII γ (Tsol ),

(3)

where Sv is the resulting Fuzzy Mixed Variable (FMV) of the computed parameter, SmI is a partial contribution of the working mode I, SmII is a partial contribution of the working mode II and SmIII is a partial contribution of the working mode III. The partial contributions simply represent the known values of the computed controller parameter associated with the analyzed working modes. Linear PI controllers were implemented over the working modes of the heating/cooling process. The sol-air temperature Tsol was selected to play the role of a single scheduling variable. A great advantage of the FMGS technique is that the Principal Fuzzy Mixing Equation can be used not only for scheduling controller gains but also for fuzzy mixing fluids flowing from the heat/cool sources. Such mixing of flowing fluids is used to additionally optimize the supply fluid temperature, which carries the heat inwards and outwards of the wall heat exchanger. The result of FMGS was the predicted version of the water supply temperature Tspred, which was equal to the water supply temperature Tsr, and mass flow rate m (kg/ (m2·s)) corresponding to the water velocity vsr. The detailed description of the FMGS method was presented in [26,33]. There exist five practical limitations in keeping up the constant TBsurface temperature Tb in high thermal-capacitance wall components:

(2)

The aim is to achieve such a quantity and direction of the heat flux ̇ (t ) so that the indoor air temperature Ti tends toward the set-point Qind ̇ (t ) depend on the Ti,ref. The quantity and direction of the heat flux Qind temperature difference between the surface averaged temperature Tb ̇ (t ) constantly and indoor air temperature Ti. Hence, the heat flux Qind varies depending upon the indoor air temperature. Under certain coṅ (t ) may be reditions, the direction of the vector of the heat flux Qind versed. As a result, the heat can be rejected to or extracted from the indoor air naturally or by changing the set-point Tb,ref of the TB-surface temperature at each time step in the year. In order to maintain the temperature Tb very close to its set-point Tb,ref, TB needs to reject or extract the proper amount of energy to or from the wall. Assuming the constant water outlet temperature for the given time step, the energy amount which is exchanged in wall heat exchangers during a certain time step depends on the water velocity vsr and inlet water temperature Tin . The inlet water temperature Tin is approximately (neglecting heat

– the supply system is based on two variable-temperature heat sources (with different temperatures: Low-Temperature Zone (LTZ) working as a cooler and High-Temperature Zone (HTZ) working as a heater) and one constant-temperature virtual heat source (so-called

Fig. 2. Flow chart of EFMGS control system. 5

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and the water supply temperature Tsr. These relationships were used in the procedures to set the valves. The TM module was solely run once before a start of TBS or once after changes of a hydraulic system. In summary, TB used two sources: LTZ and HTZ. The LTZ heat sink operated as a cooler and the HTZ heat source as a heater. In general, the operating fluid (water without anti-freeze) flowed through LTZ and HTZ zones at the same time. The ‘cold’ water from LTZ and ‘hot’ water from HTZ were mixed in the mixing device to produce the predicted temperature Tspred for the current Tb,ref. In practical applications, TB changed first the velocity of the supply water and next mixed the fluids to reach the predicted water temperature Tspred for the given velocity vsr . EFMGS was implemented in a computer program called the SVC controller.

Medium-Temperature Zone (MTZ)), – the mass fluid flow ratio may vary in a strictly limited range (e.g. from 0.0 kg/(m2·s) up to 300.0 kg/(m2·s)), – the inverse fluid flow direction is prohibited, – the high thermal capacitance of a building component, intensive solar radiation, and variable fluid mass-flow rates give rise to unpredictable changes in the internal energy system and – the hydraulic system characteristics are unknown. To overcome these limitations, the original concept of FMGS [33] was extended by developing both the Expert Module (EM), Reset Module (RM) and Tuning Module (TM). The improved and extended version of FMGS was called the Extended Fuzzy Mixing Gain-Scheduling (EFMGS) method (Fig. 2). The rule-based expert system EM was developed to control a water flow direction and limitations of a water mass flow rate. The knowledge in EM was represented in the form of a fundamental knowledge base consisting of rules of inference. The classical IF THEN inference rules were employed to correct the water mass flow rate m (corresponding to the water velocity vsr ) in the heat exchanger sections. The algorithm (so-called the Reset Module) was developed to automatically maintain the indoor air temperature Ti close to the set-point by resetting the set-point Tb,ref. The algorithm was a combination of a simple ON/OFF control scheme with hysteresis and a rate-limited reset set-point loop [36]. The time from the reset start to the reset complete was defined as the reset time, equal to N × h2, where N was the number of steps and h2 was the sampling interval for reset point that was different from the system sampling h1 time (60 s). The changes at each step ΔTb,ref were equal to each other. The parameters were arbitrarily assumed: N = 4, h2 = 900 s and ΔTb,ref = 0.5 °C. In practice, the indoor air temperature Ti can be maintained close to the set-point Ti,ref by manually or automatically changing the set-point Tb,ref. The most important operations of a control system for supplying a precise amount of heating/cooling to the wall heat exchangers are: a) water mixing supplied from LTZ and HTZ and b) setting a predicted water velocity in wall heat exchangers. Since the characteristics of the hydraulic system were unknown in practical applications, the Tuning Module (TM) was developed. The main task of TM was to determine the relationship Rm (am, vsr) between the active flow area of the main valve and the water velocity vsr in wall heat exchangers, as well as the relationships RLTZ (aLTZ, Tsr) and RLTZ (aHTZ, Tsr) for the given water velocity vsr between the active flow areas of valves in LTZ and HTZ zones

3. Test residential house and measurement methodology In order to study the performance of the energy management technique TB, the comprehensive field investigations were carried out in the scale 1:1 in a residential, two-family detached test house that was designed and constructed for investigative purposes. The test house was located in Warzno (northern Poland, 54°26′14″ - N 18°21′00″ E) close to a lake (Fig. 3). The surrounding terrain was leaning towards the lake. The test house was geographically oriented in a south-north direction and it was divided into two apartments (Fig. 4a). One apartment, called the test apartment, was designed to study the TB performance. The second one, so-called the non-test apartment, was intended for the residence only (not for measurements). Thus, the performance of TB could not be compared with a heating system installed in the non-test apartment. The apartment arrangement was mirrored in the horizontal plane (Fig. 4a). The pipe-embedded wall heating/cooling system, called Thermal Barrier System (TBS), was installed in one apartment (test apartment) and was designed to meet TB requirements. The total heating area of the test house was 281.6 m2 and the total heating volume was 789.3 m3. The house had no basement (Fig. 4b). The roof was a double-slope structure with the tilt angle of 40°. The test house was constructed from prefabricated components developed by SEWACO Inc. [34,35] and assembled on the construction site. The external walls, internal load bearing walls, ceilings and roof were composed of prefabricated composite panels. In general, the composite panels were the two-layer structures. The external walls were 0.06 m thick consisting of

Fig. 3. View of two-story and two-family residential test house in Warzno (Poland). 6

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Fig. 4. Description of test house: a) horizontal cross-section, b) vertical cross-section, c) heat exchangers embedded in ground (vertical cross-section), d) location of sensors for measurement of indoor/outdoor climate conditions and external wall temperatures.

value of external walls and roof of 0.157 W/(m2·K) and 0.128 W/(m2·K), respectively. Additionally, the test house was equipped with the natural stack multi-chimney ventilation. Waste air was exhausted through chimneys in both apartments. However, in the test apartment, wall steel sheet channel air ducts were installed (Fig. 4a, Fig. 4b) with rectangular cross-sections measuring 2 × 40 cm2. These air ducts were situated inside the external walls in the EPS layer (Fig. 4b) where they allowed fresh air to flow into the indoor zone. The fresh air flowing along the duct was initially warmed up in the winter or cooled down during the

reinforced concrete covered by insulating expanded polystyrene (EPS) layer measuring 0.24 m thick. The loads were carried through a monolithic load bearing frame made of reinforced concrete beams and columns. The external wall composite panels were not designed to carry loads. The test house was founded 2.4 m below the ground level on concrete foundations. The thermally insulated foundation walls and ground slab created a thermally separated space for the ground heat storage system (Fig. 4c). The test house was well thermally insulated, with an average U-

7

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Fig. 4. (continued)

conditions and heat loads were assumed by the measured data. The TB performance was defined by the measured heat quantity, supplied and extracted to and from wall heat exchangers. The performance of the SVC control system was expressed in terms of the SVC ability to maintain the TB-surface temperature Tb close to the set-point Tb,ref. The performance and stability were investigated based on Tb variations under variable heat loads. The impact of TB on indoor climate conditions was expressed in terms of the system capability to maintain the indoor air temperature in the assumed range. In the summer, two time-periods were compared. TBS was active during the first time-period, and inactive during the second one while the test apartment was naturally conditioned.

summer by the wall heat exchangers and next entered the indoor zone. The waste indoor air was exhausted through the chimney ducts to the ambient air. During the 17-month time period of the measurement campaign, different test procedures were conducted, including tests on the effectiveness of various control modes (manual and automatic). The measurements results were used for: – analyzing the heat exchange process in external walls with wall heat exchangers and – studying the impact of TB on indoor climate conditions in the winter and summer, – studying the performance and stability of the SVC control program.

4. Installation of TBS Numerical simulations were employed to reproduce the heat exchange process in external walls. Within the simulations, boundary

TBS was installed in the test apartment while the second apartment 8

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the solar collector down to the heat exchanger in GHSS. The heat stored in GHSS was extracted by a simple multi-sectional pipe system (Fig. 4c). The Low-Temperature Zone (heat sink) of TBS was created in the ground surrounding the test house (along with the west-oriented external wall only). The heat was exchanged by the ground heat exchanger (Fig. 4c). The operating fluid flowed from the LTZ zone and HTZ zone to the mixing device. The mixing device mixed water in such proportions to control the water temperature according to predicted values. The mixing device was assembled from the following components:

was equipped only with a traditional heating system: gas furnace and radiators (without a cooling system). The heating/cooling system installed in the test house consisted of the following main components: (a) TBS in the test apartment (Fig. 4a), (b) solar collector hidden in the roof structure (Fig. 4b), (c) ground heat storage system (Fig. 4c). TBS was composed of (Fig. 5): (a) (b) (c) (d)

wall heat exchangers embedded in the external walls, heat source (HTZ zone), heat sink (LTZ zone), mixing device.

– – – –

All heat exchangers installed in the test house were serpentineshaped multi-sectional pipe systems. The pipes of the ground heat exchanger and solar collector were made of peroxide cross-linked polyethylene (2 × 25 mm) [36] and they were spaced horizontally at 0.2 m. Wall heat exchangers consisted of diffusion-tight multi-layer composite pipes (2 × 16 mm) [36] spaced horizontally at 0.1 m. In the test apartment, a hybrid heat source was used in the HTZ zone which consisted of two heat sources: a primary source and secondary source (Fig. 5). The primary heat source was an easily installed inefficient electric boiler with adjustable power and water temperature. The secondary heat source was the ground heat storage system (GHSS) that was situated underneath the test house and supplied from the solar collector. Depending on the heat source temperature, the control system switched the active heat source automatically. The solar collector was a heat exchanger that was hidden in the roof structure (Fig. 4b). The efficiency of the installed solar collector was low but the collector was very cheap and covered the majority of the total roof area. In addition, it did not affect the building’s aesthetics. In contrast to traditional applications, the solar collector supplied heat to GHSS only and it was not used to warm up the domestic hot water. The sections of heat exchangers were situated in the roof air space between the roof steel sheet cover and a thermally insulating layer of the roof composite panel. The heat was collected by the hidden solar collector and kept in GHSS. The operating fluid (water + anti-freeze) carried the heat from

diverter-tee system, two pressure independent balancing and control valves [37], two push-pull actuators [37], three temperature sensors.

The predicted water velocity supplied from the mixing device to the wall heat exchangers was set by the main pressure independent balancing and control valve and a push-pull actuator. The main circulator pump [38] was applied to force water flow in hydraulic circuits of TBS. The pump was set into a proportional-pressure control mode (it was not controlled by a control system). The operating fluid in all hydraulic circuits was the water without anti-freeze. The water of the predicted velocity and temperature was supplied to wall heat exchangers. The wall heat exchangers were divided into sections. Each section was independently supplied. The pipe length in the single section varied between 7.5 m and 33.6 m. The variable length of sections enabled the adaptation of wall heat exchangers to each room. The heat exchanger did not cover two rooms. To enable precise control and energy management, the maximum length of single sections was assumed as 40 m (determined with laboratory tests and numerical simulations). Due to the SEWACO system properties, the wall heat exchangers were situated in the reinforced concrete layer (Fig. 6), as far as it was possible from the internal surface of the external wall panel. The composite panels with wall heat exchangers were manufactured in the prefabrication plant and assembled on the construction site. In the test apartment, the wall heat exchangers were installed in the external walls oriented to south, west, and north in the first and second floor. The parameters of heat exchangers are presented in Table 1.

Fig. 5. Schema of TBS installed in test apartment. 9

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Fig. 6. Wall heat exchanger located in SEWACO composite panel (horizontal cross-section).

5. Measurement set-up

was measured by 3 sensors embedded in the south-oriented external wall. The SVC program read measurement data from the measurement database every 60 s (the sampling interval). The actuators were activated every second time-step (every 120 s). The control program needed the input of measurement results to control the TB surface temperature:

In the test apartment, the measurement system was integrated with the control system (Fig. 7) so that data collected by several sensors of the measurement system could be used by the control system. Measurement data and results of the controller’s inference process were stored in two databases. The measurement data were collected and managed by computer program LBX of LABEL [39], while the measurements data used by the control system and results of the controller inference process were stored in MS SQL database [40]. The data were synchronized between the databases by the DbAccess computer program developed by the first author [41]. TBS and the hidden solar collector were controlled by the SVCcontroller computer program [42] that was installed on the PC desktop computer. The PC computer was equipped with the analog-to-digital converter [43]. A total of 76 sensors were mounted within and beneath the test house. The sensors were grouped into measurement nodes equipped with data acquisition modules and connected with the main server. Data were acquired at 1 s time steps by the data acquisition modules. The measurement system also registered the ambient climate conditions (temperature, humidity, wind speed and direction, solar radiation, barometric pressure) and indoor climate conditions (temperature, humidity, barometric pressure). Meanwhile, temperature and humidity sensors were embedded in the ground underneath the test house and in the ground surrounding the test house. These sensors were situated 6.3 m, 4.3 m, 2.2 m and 1.05 m below the ground level. The parameters of water temperature and flow in the hydraulic system were also monitored by velocity sensors situated at different points of the hydraulic system (Fig. 8). The net radiation value was chosen to register solar radiation. The sensor LB-902 of LABEL [38] was used to measure it. The sensor was designed for measurements of the net radiation (the difference between the incoming short-wave and outgoing long-wave background radiation). Thus, in the absence of solar radiation, the sensor measured the long-wave radiation only and recorded it with a minus sign. The list of sensors is presented in Table 2. The measurement system registered the sol-air temperature that was used by the control system as the input data. The sol-air temperature is not a physical value that can be measured directly. Instead of measuring the dry-bulb air temperature and solar radiation flux (to calculate the sol-air temperature), a special sol-air temperature sensor was developed by the authors. The sensor was located on a wall oriented to the south and indirectly measured the sol-air temperature. The temperature Tb

– – – –

temperature Tb of the TB surface, indoor air temperature Ti, sol-air temperature Tsol, water temperature TLTZ of the ‘cold’ water flowing from the LTZ zone to the mixing device, – water temperature THTZ of the ‘hot’ water flowing from the HTZ zone to the mixing device, – water temperature Tsr in the mixing device outlet and – water flow velocity vsr in the inlet to wall heat exchangers.

The hydraulic circuits of TBS were controlled by pressure independent balancing and control valves and push-pull actuators (Fig. 7). The push-pull actuators were set in a proportional mode (0–10 V). The SVC program also controlled the solar collector. The simple ON/OFF control algorithm with a hysteresis was implemented to control the solar collector. 6. Measurement results The measurements lasted 17 months, starting on the 1st of December 2015 and ending on the 1st of May 2017. The following systems were tested and monitored: – – – –

TBS, hidden solar collector, ground heat storage system and natural ventilation system with an initial warm-up of the fresh air.

6.1. Measurement campaign and climate conditions According to the Köppen-Geiger world map of the climate classification, the climate in Warzno is “cold, dry winter, warm summer” [44]. The basic climate characteristics are presented in Figs. 9 and 10. In Fig. 9, a technical break (the measurement system was stopped) caused the lack of data during the period from the 28th of November 2016 to

Table 1 Parameters of heat exchangers. System

Pipe wall thickness diameter

Number of sections

Horizontal spacing

Total length

Ground heat storage system Low-temperature zone Wall heat exchangers

2 × 25 mm 2 × 25 mm 2 × 16 mm

8 2 25

0.2 m 0.2 m 0.1 m

778.0 m 312.0 m 609.0 m

10

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Fig. 7. Schema of measurement and control system installed in test house.

divided into four stages:

the 21st of December 2016. The air temperature (dry-bulb) varied in the range of (−16.2 °C, 31.6 °C) (Fig. 9), the wind speed varied in the range of (0.0 m/s, 28.4 m/s) (Fig. 10a) and the solar net irradiance varied in the range of (−615.0 W/m2, 976.0 W/m2) (Fig. 9). The wind speed was mostly below 5 m/s, typical for low-wind conditions (Fig. 10a). The dominant wind direction was west-southwest (Fig. 10b). During the test period, the relative humidity of the ambient air strongly varied (from 15.1% up to 98.1%). The campaign was

stage I: TB tests in the winter (started on the 1st of December 2015 and ended on the 31st of March 2016), stage II: TB tests in spring/summer conditions with the manual mode of the reset set-point of the TB-surface temperature (started on the 1st of April 2016 and ended on the 24th of July 2016), stage III: Tests of indoor climate conditions with the inactive TBS

Fig. 8. Location of sensors in hydraulic system in test house. 11

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Table 2 List of sensors [39] used in measurement system. Measured variable

Sensor type

Range

Accuracy

Temperature (in ground and water) Temperature Ground humidity Air temperature Relative humidity of air Air velocity

LB-710T (Pt1000) LB-711 multi-port thermometer module LB-797 LB-710R LB-710R LB-801A

Barometric pressure Wind speed Wind direction Solar net radiation

LB-716 LB-747 LB-747 LB-902

(−99.9 °C, +199.9 °C) (−99.9 °C, +199.9 °C) (0.0%, 100%) (−99.9 °C, +199.9 °C) (1.0%, 100%) (0.01 m/s, 2.0 m/s) (0.05 m/s, 10.0 m/s) (2.0 kPa, 200.0 kPa) (0.5 m/s, 90 m/s) (0.0°, 359.9°) (−2000 W/m2, 2000 W/m2)

0.1 °C 0.1 °C 0.1% 0.1 °C 0.1% 0.01 m/s 0.1 m/s 2% 0.1 m/s 1.0° 1.0 W/m2

pressure difference of 50 Pa.

(no cooling) in the summer (started on the 25th of July 2016 and ended on the 17th of September 2016), stage IV: TB tests in winter/spring conditions with the automatic mode of the reset set-point of the TB-surface temperature (started on the 25th of December 2016 and ended on the 1st of May 2017).

6.2. Characteristics of heat exchange process in external wall The heat supply to wall heat exchangers was synchronized with variations of heat loads affecting the external wall. Every 120 s, the system supplied or extracted the least amount of heat to/from wall heat exchangers to keep up the temperature of the TB-surface close to its setpoint. A temporary heat balance of the external wall (Eq. (1)) strongly depended on the thermal capacitance of materials. The larger thermal capacitance, the less heat should be supplied to or extracted from the wall heat exchangers. A number of parameters were monitored and measured to study the heat exchange process in the external wall with wall heat exchangers (Fig. 11). The sensors measured the indoor air temperature, ambient air temperature, wind speed and direction, solar net irradiation, temperature of the internal surface of the external walls and temperature of the TB-surface. Numerical heat transfer simulations were employed to reproduce a time-variable process of the heat transfer in the external wall. The measurement data were used to define boundary conditions and heat loads. The heat transfer was modeled in a horizontal cross-section of the external wall at the height of 1.5 m (Fig. 12). A two-dimensional (2D)

During the period from the 28th of November 2016 to the 21st of December 2016, the measurement system was inactive due to some technical reasons. To study the influence of TB on the indoor air temperature, the indoor air temperature was controlled by manually changing the set-point Tb,ref during measurement stages. The test apartment was unoccupied during the entire measurement program and windows remained closed to avoid refreshing the air in the room. There were no internal heat sources except for the measurement and control hardware equipment powered by electricity. The comfort temperature (set-point) of the indoor air was set at 19 °C and 21 °C during the winter and summer, respectively. However, the lack of activity of residents (e.g. night ventilation by opening windows) and external shadings increased the influence of solar radiation on the indoor air temperature. At the end of Stage I, the air-tightness test was performed. The Blower Door method [45] was applied to find air leakages through the house envelope. The measured value of air-tightness n50 was 3.55 h−1 at the

Fig. 9. Time variations of air temperature (dry-bulb) and incident solar net radiation during test period (technical break is marked by green vertical stripe). 12

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Fig. 10. Wind speed (a) and wind direction (b) during test period.

On the external wall surface, the heat transfer conditions considered the net solar radiation and convection (the positive value of heat flux was directed to the ambient air)

model of the unsteady heat transfer was assumed:

cp ρ

dT ∂ 2T ∂ 2T ⎞ = −λ ⎛ 2 + + qsup (t ), dt ∂ x ∂y 2 ⎠ ⎝ ⎜

⎟

(4)

λ

where cp is the specific heat at the constant pressure (J/(kg·K)), ρ is the density (kg/m3), T is the temperature (K), t is the time (s), qsup is the heat supplied/extracted by the wall heat exchanger (W/m3) and x and y are the spatial coordinates (m). The density and heat transfer coefficients of built-in materials were determined in laboratory tests, while the specific heat was taken from the literature. Collected data were used to define temperature and heat flux boundary conditions, as well as the convective component of the heat transfer coefficient. The temperature Tsi (t ) of the internal wall surface was defined as:

Tsi (t ) = fsi (t )

∂T = −Qṙ (t ) + he, conv (t )·(Tse (t ) − Te (t )), ∂y

(6)

where Qṙ (t ) the measured radiative net heat flux (short-wave radiation minus long-wave radiation) for the exposed façade (W/m2), he, conv (t ) is the convective part of the heat transfer coefficient (W/(m2·K)), Tse (t ) is the temperature of the external wall surface (°C) and Te (t ) is the measured temperature (dry-bulb) of the ambient air (°C). The convective heat coefficient depended on the wind speed and was defined by the empirical formula in [46]:

he, conv (t ) = max ⎡5, ⎢ ⎣

(5)

8.6ws (t )0.6 ⎤ , ⎥ l 0.4 ⎦

(7)

where ws(t) - the measured wind speed (m/s) and l - the cubic root of

where fsi (t ) is the measured temperature. 13

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Fig. 11. Measurement points in external wall (horizontal cross-section view).

where Qint (t ) - the wall heat accumulated (J/m2), Qci̇ (t ) - the heat flux ̇ (t ) - the heat flux on the on the internal surface of the wall (W/m2), Qce ̇ (t ) - the meaexternal wall surface (convective part) (W/m2) and Qspl sured heat supplied or extracted from 1 m2 of the wall in the time unit (W/m2). In the simulation of the heat balance, the following convention of signs was assumed: the positive sign of the heat flux meant the heat transfer into the wall and the negative sign of the heat flux meant the heat transfer out from the wall. During the winter from the 1st of January 2016 to the 31st of March 2016, the internal energy Qint of the external wall slightly varied in the range of (−810.8 kJ, 248.2 kJ) (Fig. 13a). The total (cumulated) internal energy QT , int oscillated around 0.0 kJ and decreased slightly with increasing the ambient temperature Te. This effect was the result of control system operation. The control system decreased the temperature of water supplying the wall heat exchangers due to an increase in the ambient air temperature. Thus, the internal energy decreased and less heat was needed to maintain the comfort temperature in the indoor air. When analyzing the variations of the temporal components of heat balance (Eq. (9)), the thermal capacitance effect could be observed. During the winter, the solar energy that accumulated in the wall during the daytime affected the heat transfer process. Fig. 14b presents the variations of the heat balance components in the external wall on the 17th of February (2016). The relatively high solar net radiation (the

the test house volume (m). The commercial software ABAQUS [47] was used to simulate a heat transfer process in the external wall. To define boundary conditions (the heat loads and the internal heat source in the numerical model), the procedures were written in the FORTRAN programming language. The numerical simulation covered the time period of 17 months of measurements. For verification of simulation results, the measured temperature Tb was compared with the simulation result. The median value of the difference between the measured and computed temperature Tb was solely 0.041 °C with a standard deviation of 0.319. The basic concept of TB was to use the external wall as the temporal heat storage by synchronizing the heat supply to the external wall with variable heat loads. Hence, the variations of the internal energy Qint in the external wall were close to zero. This synchronization was reached by keeping the average temperature Tb close to the set-point. The total change of the internal energy QT,int was calculated as follows:

QT , int =

∫ Qint (t ) dt,

(8)

t

while the temporal variations of the internal energy Qint(t) as:

dQint (t ) ̇ (t ) + Qspl ̇ (t ), = Qci̇ (t ) + Qṙ (t ) + Qce dt

(9)

Fig. 12. Two-dimensional model of unsteady heat transfer in external wall (units in (mm)). 14

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Fig. 13. Variations of internal energy QT , int and ambient air temperature Te in south-oriented external wall: a) in winter from 1st of January 2016 to 31st of March 2016 and b) in spring/summer from 1st of May to 23rd of July 2016.

greatest value of 281.2 W/m2) was observed for 5.5 h (from 8:40 o’clock to 14:10 o’clock) and strongly influenced heat balance in the ̇ was 39.6 W/m2 external wall. The mean value of the heat supply Qspl during the night and strongly decreased to 11.8 W/m2 during the time period of intensive solar radiation. During the period of intensive solar radiation, the heat flux Qci̇ transferred to the indoor air decreased almost to zero. The indoor air

temperature was maintained near the set-point (Fig. 14a) by means of solar radiation instead of heat transferred from the external wall. The changes in the total internal energy QT , int followed the temperature changes Tb (Fig. 14c). The control system kept the TB surface temperature Tb variations very close to the set-point of 21.5 °C with the accuracy of (−0.2 °C, 0.2 °C). Due to the increase of the ambient air

15

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Fig. 14. Changes of: a) ambient air temperature Te, indoor air temperature Ti and solar net radiation, b) heat balance components and c) total internal energy QT,int and TB surface temperature Tb in external wall in winter on 17th of February 2016.

temperature Te (from −6.0 °C to 0.8 °C) and the solar net radiation Qṙ (up to 281.2 W/m2), the control system decreased the temperature Tb (down to 19.7 °C) to compensate the relatively high heat load (Fig. 14c). The characteristics of the heat balance during the summer were very different than in the winter, however, like in the winter, the total (cumulated) internal energy QT , int of the external wall varied in the range of (−233.4 kJ, 514.1 kJ) and oscillated around 0.0 kJ (Fig. 13b). This difference was caused by changing climate conditions which dominated the cooling function of TBS. Fig. 15b presents the changes in the heat balance components during two summer days of extreme heat loads (presented in Fig. 15a). The ambient air temperature (dry-bulb temperature) varied from 17.7 °C to 31.3 °C on the 24th and 25th of June 2016, while the radiative heat flux on the external wall surface varied from −181.0 W/m2 to 594.4 W/m2. During these days, the set-point of the indoor air temperature was 21.5 °C while the indoor air temperature Ti varied in the range of (20.9 °C, 21.6 °C). This combination resulted in a slight overcooling of the indoor room. During the time period from the 24th to 25th of June 2016, the heat was solely extracted from the wall to keep the temperature Tb close to the set-point of 19.6 °C. It was also observed that a very small amount of heat was extracted from the wall to keep the indoor air temperature Ti close to the set-point. The heat ̇ extracted from the wall by TBS was in the range of (−19.7 W/ flux Qspl ̇ = −11.8 W/m2 . m2, 0.0 W/m2) with the mean value of Qspl During the night, the wall was cooled mainly by the long-wave radiation to the surroundings with a mean value of Qṙ equal to 38.2 W/m2

(Fig. 15a). The total internal energy QT , int was in the range of (−128.8 kJ, 185.7 kJ) (Fig. 15c) while the control system kept temperature Tb variations close to the set-point of 19.5 °C with the accuracy of (−0.5 °C, +0.4 °C). It should be noted that the convective heat flux ̇ on the external wall surface was very close to zero and its value was Qce merely disturbed by intensive solar radiation (short-wave radiation). This relationship was season-independent. The uniformity of the surface temperature of the external wall is very important. Large variations in surface temperature may adversely affect the efficiency of the heat exchange process. Thus, the temperature sensors were situated in the south-oriented external wall along the vertical line at the height of 0.1 m, 1.4 m and 2.7 m above the floor and far away from thermal bridges. The measurement results showed that the spatial distribution of the wall heat exchangers in the external walls was very effective and resulted in very small temperature variations of less than 1.0 °C on the internal surface of the external wall. During measurements, the control system ran in a single zone mode, which means that the indoor space was treated as a single control zone. The sol-air temperature sensor was located on the south-oriented external wall only. Hence, the set-point of the TB-surface temperature Tb was the same for all external walls, oriented in the south, west and north geographic directions. Nevertheless, the temperatures of the internal surface of the external walls (differently geographically oriented) were very close to each other. During the winter and summer, the temperature difference between the variously oriented walls did not exceed 0.6 °C and 0.4 °C, respectively. In the winter, the highest temperature was measured on the southern wall. The slightly lower 16

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Fig. 15. Changes of: a) ambient air temperature Te, indoor air temperature Ti and solar net radiation Qṙ , b) heat balance components and c) total internal energy QT , int and TB surface temperature Tb in external wall in summer on 24th and 25th of June 2016.

control strategy should be applied instead.

temperature was measured on the northern wall and the lowest temperature was registered on the western wall. These temperature differences resulted from different wind directions, with the dominant wind direction pointing west-southwest (Fig. 10b). In the summer, the highest temperature was measured on the western wall. Meanwhile, a slightly lower temperature was registered on the southern wall and the lowest temperature was measured on the northern wall. The different temperature distribution in the summer was caused by a time shift of the peak air temperature and solar radiation values. In the summer, the peak heat load was between 12 and 14 h, and in the winter between 9 and 12 h. During the summer peak heat load, the sun position caused the solar radiation mainly affected the west wall. Opposite to the summer peak heat load, the sun position in the winter caused solar radiation mainly affected the south wall. However, one can note that the temperature differences were insignificant. The small temperature differences were caused by a difference in heat losses in the distribution system. Additionally, the distances between the technical room and wall heat exchangers embedded in the south, west and north walls were different with the largest distance measured between the technical room and south wall and the shortest distance between the technical room and north wall. This resulted in slightly different supply water temperatures in various geographically oriented heat exchangers. As a result, the wall surface temperatures were almost equalized. Over the entire measurement time-period, the water temperature Tin in the inlet of the wall heat exchanger embedded in the south-oriented wall was very low and changed in the range of (7.9 °C, 25.3 °C). Measurement results confirmed that the single zone control strategy was very effective for small houses. However, for multi-family houses, the multi-zone

6.3. Control system performance and stability During measurements, a number of tests of the control system were conducted. The test procedures included the studies of the overall performance and stability, and the control algorithms: the FMGS control method, the FMGS expert system and the reset set-point algorithm. The FMGS expert system is the rule-based expert system which is an integral part of the FMGS control method. FMGS expert system improves control quality by considering technical limitations of the hydraulic system. For example, despite the fact that water flow in a pipe system cannot be reversed, the control system attempts to do it. The test procedure was divided into two tests so-called the manual mode test (MM test) and the automatic mode test (AM test). The MM test was conducted during the winter from the 1st of February 2016 to the 29th of February 2016 and during the summer from the 2nd of June 2016 to the 24th of July 2016. The AM test was performed from 1st of January 2017 to the 10th of May 2017. The MM test concerned the FMGS control method with the manual control of the set-point of the indoor air temperature. During the winter, the climate inference rules of the FMGS expert system were activated. The climate inference rules analyzed the ambient climate data. During time periods of a very low ambient air temperature and very intensive solar radiation, the climate inference rules could conclude that the water flow can be stopped and the wall can be cooled only by the convective heat transfer to the ambient air. This strategy was expected to result in a strong decrease of the temperature Tb below 17

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The system controlled the water supply temperature Tsr and velocity vsr to supply or extract the predicted energy amount to or from wall heat exchangers (Fig. 18). The prediction results influenced the valve settings that were updated every 120 s. Hence, both the water temperature and the water velocity might change strongly with time. Observations show that during intensive solar radiation (from 11:50 to 13:09), the control system stopped the water flow. After sunset, the control system reduced the water velocity vsr. This velocity reduction was due to the storage of a certain quantity of solar energy during the daytime. The supply temperature Tsr was almost constant (22.0 °C) but dropped during intense solar radiation (water flow was stopped) when the control system reduced temporarily the water velocity vsr to speed up the cooling effect. At that time, the amount of energy supplied to the wall heat exchangers was regulated primarily by changing the water velocity. During the summer between the 2nd and the 23rd of June, the setpoint of the control variable Tb was set to 19.2 °C. The ambient air temperature Te varied in the range of (3.0 °C, 29.2 °C) with the median value of 15.4 °C. The maximum incident solar radiation was 862.0 (W/ m2). During this time period, the control variable Tb varied in the range of (18.8 °C, 19.6 °C) (Fig. 19a). The assumed set-point of the temperature Tb resulted in slight overcooling of the indoor air. During the following days, the climate became slightly warmer (Fig. 19b). The median value of the ambient air temperature increased by 1.3 °C and the peak value reached 31.6 °C, while the peak value of the incident solar radiation reached 976.0 W/m2. Due to a change of climate parameters and the indoor air overcooling/overheating, the setpoint of the control variable Tb was updated three times in the time period between the 24th of June to the 24th of July and was set at 19.8 °C for 6 days, next at 20.2 °C for 9 days and finally at 19.8 °C (Fig. 19a). It resulted in an increase in the median indoor air temperature Te up to 21.0 °C in the test time period. The MM test procedure in the summer was the same as in the winter with the only difference being an extra test program for estimating the highest temperature of the supply water. The test program was run during the hottest days of the summer and the results of the control system operation on the 25th of June are shown in Fig. 20. On the 25th of June, the ambient air temperature varied in the range of (17.7 °C, 30.9 °C) and the peak incident solar radiation was

its set-point to cool down the wall very fast without energy consumption. The AM test concerned the automatic control mode (reset set-point algorithm activated) of the indoor air temperature. The test was conducted from the 1st of January 2017 to the 10th of May 2017. In the MM test, the set-point of the indoor air temperature was undefined. Instead, the set-point of the control variable Tb was updated manually to maintain the preferable indoor air temperature. Regardless of the test procedure, the control variable Tb was controlled automatically by the FMGS method. In the winter of the MM test, the set-point of the control variable Tb was set to 21.5 °C. The manual control strategy was assumed to keep the indoor air temperature close to 19.0 °C with the accuracy of ± 0.5 °C. During the time period of the MM test in the winter, the control variable Tb varied in the range of (21.1 °C, 21.7 °C) excluding the time periods when the expert system stopped water flow in the hydraulic system to cool down the external wall naturally, without the forced heat extraction from the wall (Fig. 16). The indoor air temperature Ti varied in the range of (18.3 °C, 19.7 °C) and slightly exceeded the assumed limit. On the 12th of February, the temperature Ti dropped down to 17.8 °C due to some technical problems with the electricity supply. It was observed that there was no need to update the set-point of the temperature Tb during the measurement time period since the temperature Ti slightly exceeded the limit (only by 0.2 °C). The MM test procedure, conducted during the winter, included a test of the expert system of the extended FMGS control method. The climate inference rules were activated in the winter and results of the expert system operation on the 21st of February are presented in Fig. 17. Although the ambient air temperature Te varied in the range of (2.5 °C, 6.7 °C) and was relatively low, the incident solar radiation reached an intense peak winter value of 459.1 W/m2. Close to the peak value of the incident solar radiation, the expert system stopped the water flow for 1 h and 20 min. As a result, the temperature Tb decreased by 0.5 °C, while beyond this time period, the temperature Tb was kept close to the set-point of 21.5 °C with the accuracy of ± 0.1 °C. Even though the temperature Tb decreased, the indoor air temperature Ti slightly increased by 0.2 °C but it did not exceed the limit. These measurement results confirmed the effectiveness of the inference process within the EM expert system. During the entire MM test in the winter, the control system demonstrated high performance and stability by maintaining the temperature Tb within ± 0.4 °C.

Fig. 16. Time variations of control variable Tb in south-oriented wall and indoor air temperature Ti during MM test in winter from 1st of February 2016 to 29th of February 2016. 18

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Fig. 17. Time variations of control variable Tb in south-oriented wall, ambient air temperature Te, indoor air temperature Ti and solar net radiation Irad during first test in winter on 21st of February 2016.

Fig. 18. Time variations of measured water supply temperature Tsr (red colour), velocity vsr (blue colour) and velocity vsr fitting line (brown colour) during first test in winter on 21st of February 2016.

650 W/m2 (Fig. 20a). The highest water supply temperature of 19.0 °C was recorded (Fig. 20b). It resulted in a very small increase of the temperature Tb by only 0.1 °C. As a result, the indoor air temperature increased from 21.0 °C (during the night) up to 21.6 °C (in the afternoon). The night-to-day difference of the indoor air temperature was thus merely 0.6 °C. Regardless of the very high heat load and the very high temperature of the supply water, the control system was able to maintain the TB-surface temperature very close to its set-point and close to the assumed thermal comfort level. However, the maintenance of thermal comfort required a relatively high velocity of the supply water (0.18 m/s) throughout the entire time period. Two velocity drops to zero were observed (Fig. 20), which simply meant that water flow was stopped by the control system. The operation of the control system in the summer is presented for the measurement data of 23 June (Fig. 21). On the 23rd June, the peak value of the net solar radiation reached 632 W/m2 and the ambient air

temperature varied in the range of (14.8 °C, 29.3 °C). Large variations of thermal loads were compensated by a control system. For example, from 4:20 o'clock, the control system began to diminish the supply temperature Tsr down to 16.8 °C (at 8:13). The average water velocity was also reduced to 0.09 m/s. The process of reducing heat extraction from wall heat exchangers began earlier than the intensity of solar radiation increased significantly (at 7:10). This fact may be explained by a delayed effect of removing heat from the wall at night. The lack of heat stored in the wall made it possible to reduce the heat quantity extracted from the wall in the time period of intensive solar radiation. The AM test focused on the automatic control of the indoor temperature was conducted from the 1st of January (2017) to the 10th of May (2017) (Fig. 22). The algorithm of the reset set-point loop was activated as well as the climate inference rules of the FMGS expert system. The results of the control system operation are presented during the time period of the first 10 days of January 2017, when the lowest 19

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Fig. 19. Time variations: a) control temperature Tb in south-oriented wall and indoor air temperature Ti and b) ambient air temperature Te and incident solar net radiation Irad during MM test in summer from 2nd of June 2016 to 24th of July 2016.

temperature of −12.3 °C was recorded (Fig. 22a). During this time period, the ambient air temperature Te varied in the range of (−12.3 °C, 4.5 °C) and the peak value of the incident solar radiation was 234.0 W/ m2. The control system reset the set-point of the temperature Tb only 5 times in 10 days. As expected, the set-point was reset during time-periods of the significant variations of the ambient air temperature. The set-point of the temperature Tb was reset from 20.2 °C to 20.6 °C and next to 21.0 °C (Fig. 22b). This value of the set-point was not changed for about 3 days until the ambient air temperature started to quickly increase. The control system responded by decreasing the set-point down to 20.4 °C followed by a decrease to 20.0 °C. During the timeperiods of the constant value of the set-point, the temperature Tb was kept with the accuracy of ± 0.4 °C. The resetting of the Tb set-point resulted in maintaining the indoor air temperature Ti with the accuracy of ± 0.3 °C. It should be noted that the indoor air temperature was very stable for several days and the resetting of the set-point was very rarely required. This stability was achieved by synchronizing the heat supply strategy with the heat load variations in a combination with the large

wall heat capacitance. Measurement results confirmed that the EFMGS control method is very stable. The impact of temporal disturbances on the controlled variable was unnoticeable. Also, the high efficiency of the EFMGS control method was achieved by synchronizing the heat supply strategy with the heat loads variations in a combination with the large heat capacitance of the wall. It was observed that the control system was able to maintain the assumed thermal comfort even if the automatic control mode of the indoor air temperature was inactive. Over the time period of the measurement campaign, the water temperature measured at the inlet of the wall heat exchanger embedded in the south-oriented wall was very low and varied in the range of (7.9 °C, 25.3 °C). A summary of the measurement results is presented in Table 3.

6.4. Impact of TB on indoor thermal conditions in winter The effect of TB on the indoor thermal conditions in the winter was tested from the 1st of January 2017 to the 31st of March 2017. During 20

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Fig. 20. Time variations: a) control variable Tb, incident solar net radiation Irad, water velocity vsr in the inlet of the wall heat exchanger and b) control variable Tb, indoor air temperature Ti, water temperature Tin in inlet of wall heat exchanger in south-oriented wall in summer on 25th of June 2016.

It was observed that intense solar radiation (sunny weather) caused high activity of the control system. The intense solar radiation occurred on the 11th and 12th of February and varied in the range of (−64.0 W/ m2, 194.0 W/m2). At this time, the Tbref temperature (set-point of Tb) was reset nine times. The measurement results showed that TB very efficiently kept up the assumed thermal comfort of the indoor environment during the winter. The daily variations of the indoor air temperature did not exceed 1.0 °C.

this time period, the automatic control mode of the indoor air temperature was active. For further analyses, the time period from the 30th of January 2017 to the 12th of February 2017 was chosen (Fig. 23) with a comfort temperature of the indoor air set to 19 °C. Throughout the test, the climate conditions varied strongly with the ambient air temperature varying in the range of (−12.0 °C, 2.1 °C) and the solar net radiation varying in the range of (−70.0 W/m2, 243.0 W/m2). Despite the strongly variable climate conditions, the indoor air temperature varied in the range of (18.5 °C, 19.5 °C) (Fig. 24) and was maintained close to the set-point with the accuracy of ± 0.5 °C. During the test time period, the control system reset the set-point of the temperature Tbref several times. It was observed that from the 2nd of February to the 4th of February the set-point Tbref was set to 21.0 °C and was not changed for 52 h even though the ambient air temperature varied in the range of (−8.1 °C, 0.2 °C). However, the solar net radiation was not intense (cloudy weather) and varied in the range of (−38.0 W/m2, 75.0 W/m2).

6.5. Impact of TB on indoor thermal conditions in summer The test of the effect of TB on the indoor thermal conditions was divided into two time periods: (1) from the 1st of June 2016 to the 24th of July 2016 and (2) from the 25th of July to the 17th of September 2016. During the first period, TBS was active and the indoor climate 21

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Fig. 21. Time variations of measured water supply temperature Tsr (red color) and velocity vsr (blue color) during first test in summer on 23rd of June 2016.

in the LHZ outlet (as depicted in Fig. 8). These measurement results of the water temperature Tin showed that TBS could use even a very lowgrade sink source to keep up the indoor air temperature close to the setpoint (temperature of the thermal comfort). From the 1st of May to the 31st of October, TBS was solely supplied from the low-grade renewable heat sources and sinks. The summary of the measurement results is presented in Table 4.

was controlled. During the second period, TBS was inactive and the test apartment was naturally conditioned via the natural ventilation system. For the purpose of the test, the indoor air temperature of 21.0 °C was assumed as the temperature of the thermal comfort. During the first period, the ambient air temperature varied in the range of (3.0 °C, 31.6 °C) and the peak incident solar net radiation was 976.0 W/m2 (Fig. 25). The set-point of the TB surface temperature Tb was set initially at 19.0 °C and the indoor air temperature changed in the range of (19.9 °C, 21.8 °C) (Fig. 25). Intermittently, the indoor space was slightly overcooled. Thus, after 25 days, the set-point of the temperature Tb was increased up to 20.0 °C. During the next 29 days of the test, the median value of the indoor air temperature was 20.99 °C and was very close to the assumed temperature of thermal comfort with daily variations of the indoor air temperature below 0.9 °C. To investigate indoor thermal conditions in the naturally conditioned test apartment, TBS was switched off in the second time period of the test. Due to the adverse climate conditions in August 2016 (an exceptionally cold month), the time period of the test was extended up to 59 days (from the 25th of July to the 17th of September). The ambient temperature varied in the range of (8.3 °C, 29.1 °C) while the peak incident solar net radiation was equal to 837.0 W/m2 (Fig. 26). Both the peak ambient air temperature and incident solar radiation were slightly smaller than the values recorded during the test first time-period. At the end of August and in September, the effective time of exposition to the solar radiation was shorter than during the test first time period. Hence, the ambient climate conditions were more beneficial to keep up the preferable indoor thermal conditions. Nevertheless, the indoor air temperature varied in the range of (22.1 °C, 26.2 °C) (Fig. 26). The peak indoor air temperature reached the value of 26. °C and strongly exceeded the assumed temperature of thermal comfort. The daily variations of the indoor air temperature did not exceed 1.4 °C. Measurement results showed that TB efficiently maintained the assumed thermal comfort of the indoor environment during the summer. The daily variations of the indoor air temperature did not exceed 0.9 °C. In contrast, the natural cooling of the test apartment was not able to keep up the assumed comfort level and the indoor air temperature was strongly above the limit. Over the considered time period, the water temperature Tin measured in the inlet of the wall heat exchanger embedded in the south-oriented wall changed in the range of (15.1 °C, 20.5 °C). It should be noted that Tin is the water temperature measured at the wall heat exchanger inlet is not the water temperature measured

6.6. Performance of TBS and installation costs Since the non-test apartment was intended for the residence only (not for measurements), the performance of TBS could not be compared directly with a heating system installed in the non-test apartment. However, to get a very rough estimated performance view, the measured heat/cool consumption was compared to the theoretical heat/ cool demand. The calculations of the theoretical demand were carried out in accordance with the methodology introduced in [48]. It should be noted that the methodology [48] is based on a very simple energy model and may lead to relatively large errors in the calculated heat/ cool demand. To reduce error, the following measurements results were used as the input data: the temperature of the ambient and indoor air, solar net radiation, ventilation air exchange rate and temperature. The test apartment was not occupied throughout the measurement period, hence only the energy released into space by appliances (including a computer, router, measurement data loggers, chargers, and hot water container being a part of TBS) were taken into account as the internal heat gains. The heat demand was calculated for the time period of 4 months (from the 1st of January 2017 to the 30th of April 2017). The computation results showed that the theoretical heat demand was 3366.9 kWh (23.9 kWh/m2) and the measured heat supplied to the wall heat exchangers was 2671.3 kWh (19.0 kWh/m2) over the considered time period (4 months). During this time, TBS consumed 21% less heat than the theoretical heat demand of the test apartment. The cooling demand was calculated for the time period of 54 days (from the 1st of June 2016 to the 24th of July 2016). The computation results showed that the theoretical cooling demand was 1499.2 kWh (10.7 kWh/m2) and the measured value of the heat extracted by TBS from the wall heat exchangers was 1762.0 kWh (34.2 kWh/m2) over the considered time period (54 days). During this time, TBS extracted 15% more heat than the theoretical cooling demand of the test apartment. The differences in 22

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Fig. 22. Time variations: a) control variable Tb in south-oriented wall and the indoor air temperature Ti and b) ambient air temperature Te and incident solar net radiation Irad in south-oriented wall during AM test in winter from 1st of January 2017 to 10th of January 2017.

the TBS effectiveness with other heating/cooling systems. The initial construction’s cost of the test house was also analyzed. In the cost analysis, the construction’s cost of the test house equipped with TBS was estimated and compared to the construction’s cost of the test

the energy consumption-demand were however still within the scope of the method’s error. Therefore, an advanced energy model of TBS (including a control system) will be developed and validated with the measurement results. This energy model will enable the comparison of

Table 3 Comparison of performance and stability of control system in manual and automatic mode.

Ambient air temperature Te (°C) Solar net radiation Irad (W/m2) Temperature of TB-surface Tb (°C) Indoor air temperature Ti (°C) Number of resets of Tb,ref ( −) Set-point of temperature Ti,ref (°C) Accuracy of maintaining set-point Tb,ref (°C) Accuracy of maintaining set-point Ti,ref (°C) Day-to-day accuracy of maintaining setpoint Ti,ref (°C)

Winter (manual mode) from 1st of January to 31st of March (2016)

Winter (automatic mode) from 1st of January to 31st of March (2017)

Summer (automatic mode) from 1st of June to 24th of July (2016)

(−16.2, 14.7) (−252.0, 659.0) (16.3, 22.6) (18.1, 20.1) 13 19.0 ± 0.5

(−12.4, 19.3) (−385.0, 600.0) (15.7, 22.1) (18.3, 19.9) 89 19.0 ± 0.4

(3.0, 31.6) (−305.0, 976.0) (18.8, 20.4) (19.8, 21.9) 101 21.0 ± 0.4

± 1.1 ± 0.5

± 0.9 ± 0.4

± 1.2 ± 0.4

23

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Fig. 23. Time variations of ambient air temperature Te and incident solar net radiation Irad in winter during time period from 30th of January 2017 to 12th of February 2017.

Fig. 24. Time variations of indoor air temperature Ti and TB surface temperature Tb during indoor thermal conditions test in winter from 30th of January 2017 to 12th of February 2017.

6.7. Thermal Barrier in practical applications

house equipped with the traditional heating system (variant I) and to the construction’s cost of the test house equipped with the air-conditioning system (variant II). It was assumed that the heating system in the variant I consisted of a gas furnace (heat source) and radiators without a cooling system. In variant II, a typical air-conditioning system was assumed. The cost of the measurement system and the cost of the building project were both neglected. The ready for use building’s state (without furnishing) was taken into account. It was estimated that the construction’s cost of the test house equipped with TBS was only 2.8% higher than the test house in the variant I (heating system). As compared with the initial construction’s cost of TBS with the test house in the variant II (air-conditioning system), the cost of TBS was 8.4% lower.

The measurement results confirmed that TB could be supplied from low-grade heat sources/sinks. The measured peak temperature of the supply water temperature was less than 26.0 °C in the winter and 20.5 °C in the summer. Thus, in practical applications, the use of the ground is particularly advantageous as a heat sink. Due to energy dissipation, the ground heat exchanger should be located about 2.0 m below ground level. This location enables the transfer of excess heat from the ground to ambient air in the winter. However, the use of the ground as a heat sink is limited to single-family and small multi-family residential houses. 24

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Fig. 25. Time variations of indoor air Ti and TB surface temperature Tb during first time period of indoor thermal conditions test (TBS was active).

The TB energy management technique is very flexible. TB can be applied in various configurations that significantly differ from a configuration designed in the test house. TB may use different heat sources and sinks (renewable and non-renewable). However, there are several working limitations related to performance and energy effectiveness. When using non-renewable energy sources and sinks, it is not reasonable to mix very cold water with very hot water. The results of the measurements showed that the recommended working range of the supply water temperature in HTZ (heat source) is between 20.0 °C and 30.0 °C while in LTZ (heat sink) is between 10.0 °C, 20.0 °C. However, TB does not need two separate devices for heating and cooling. Technically, TBS may be supplied by a single device (energy source) whose operating temperature range lies between 15.0 °C and 30.0 °C.

The device should be able to control the supply water temperature with the accuracy of ± 3.0 °C. The heat pump working in two modes (heating and cooling) suits these requirements. A single energy source does not affect the operation and performance of the EFMGS control method. Besides the lack of a mixing device the only difference is that EFMGS uses virtual constant temperatures of LTZ and HTZ instead of the measured values to predict the supply water temperature Tspred and velocity vsr. 7. Summary and conclusions Comprehensive field investigations were conducted to study the performance of an improved technique of energy management in a

Fig. 26. Time variations of indoor air temperature Ti, ambient air temperature Te and incident solar net radiation Irad during second time period of indoor thermal conditions test (TBS was inactive). 25

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Table 4 Comparison of impact of TB on indoor thermal conditions in winter and summer.

Ambient air temperature Te (°C) Solar net radiation Irad (W/m2) Indoor air temperature Ti (°C) Set-point of temperature Ti,ref (°C) Accuracy of maintaining set-point Ti,ref (°C) Day-to-day accuracy of maintaining setpoint Ti,ref (°C) Inlet water temperature Tin (°C) Inlet water velocity vsr (m/s)

Winter (TB active) from 1st of January to 31st of March (2017)

Summer (TB active) from 1st of June to 24th of July (2016)

Summer (TB inactive) from 25th of July to 17th of September (2016)

(−12.4, 19.3) (−385.0, 600.0) (18.3, 19.9) 19.0 ± 0.9 ± 0.4

(3.0, 31.6) (−305.0, 976.0) (19.8, 21.9) 21.0 ± 1.2 ± 0.4

(8.3, 29.1) (−615.0, 837.0) (20.8, 26.2) – – –

(9.7, 25.3) (0.0, 0.22)

(15.1, 20.5) (0.0, 0.22)

– –

pipe-embedded wall heating/cooling systems during a period of 17 months. The pipe-embedded wall heating/cooling system was controlled by a special fuzzy logic program to optimize the activity of the entire system. The following conclusions were drawn from the measurements:

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– The supply water temperature measured in the inlet of wall heat exchanger embedded in the south-oriented wall varied in the range of (7.9 °C, 25.3 °C) and (15.1 °C, 20.5 °C) in the winter and summer, respectively. The peak temperature of the supply water was very low in the winter (the peak value was 25.3 °C) and very high in the summer (but not higher than 20.5 °C). This result confirmed that TB was able to effectively exploit low-grade heat sources and sinks (e.g. ground heat/cool storage systems). – The control method was very efficient for maintaining the assumed thermal comfort, even if the automatic control mode of the indoor air temperature was inactive. Daily variations of the indoor air temperature did not exceed 0.8 °C in the winter and summer seasons. – The single-zone control strategy was very effective for single-family residential houses. The internal surface temperatures of external walls (south-, west- and north-oriented) were very close to each other, and the temperature difference did not exceed 0.6 °C during the winter and 0.4 °C during the summer. – Studying the heat transfer process through the external wall in summer and winter conditions, it was observed that the convective heat flux on the external wall surface was very close to zero and its value was only disturbed by the intensive solar radiation (shortwave radiation). This relationship was season-independent. – The spatial distribution of wall heat exchangers was very effective since the temperature variations on the internal surface of external walls were marginal (not exceeding 1.0 °C). – A modular structure of the control system enabled the individual room control with the restriction that a single section of a wall heat exchanger did not include two zones. Acknowledgements The present study was supported by the research project “Innovative complex system solution for energy-saving residential buildings of a high comfort class in unique prefabricated technology and assembly of composite panels” financed by the National Centre of Research and Development NCBR (NR.R1/INNOTECH-K1/IN1/59/155026/NCBR/12). The calculations were carried out at the Academic Computer Centre in Gdańsk. The English language editing was performed by Robert A. Caulk. References [1] Li DHW, Yang L, Lam JC. Impact of climate change on energy use in the built environment in different climate zones - A review. Energy 2012;42:103–12. [2] U.S. Energy Information Administration, Monthly Energy Review, 2015, http:// www.eia.gov/totalenergy/data/monthly/index.cfm#consumption.

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