Modeling of the heating system in small building for ...

Modeling of the heating system in small building for control Szymon OGONOWSKI Institute of Automatic Control Silesian University of Technology Akademicka 16, 44-100 Gliwice, Poland e-mail: [email protected] fax.: 032 237-21-27, tel.: 032 237-23-98

Abstract: Models identification for the heating system of small building is presented. Basic problems of data acquisition and preprocessing are detailed. Specially designed wireless data collection and control system to conduct real-world experiments are described. The model structure choice based on analysis of weather condition influences on the system performance is presented. Finally, the general structure of two-layered heating control strategy for the heating system is proposed. Instead of heating, ventilating and air conditioning (HVAC) standard, the proposed strategy controls only indoor temperature and changes the set-point according to additional measurements of indoor humidity to keep thermal comfort. Non-linear compensation of outdoor temperature and wind speed is also introduced. Keywords: heating, identification, simulation, control, adaptation.

1. Introduction Traditional heating control system in small buildings measures only indoor and outdoor temperatures. These measurements are used as the input signals of boiler control or indoor control with possibly outdoor temperature compensation. Such control systems are boiler–oriented rather then takes into account specific heating demands of a certain building. It would be very difficult to dedicate control system to a specific building due to great variety of building technologies and its dynamical properties. The robustness is then the main reason of a simple on-off control wide applications. This type of control systems neglects the building dynamical properties; only a steady state is taken into account during the controller tuning. The consequence of such boiler-oriented simplified control is the fuel over-consumption together with under and overheating. The user accepts oscillations of the resulting temperature (controlled variable), however, from the fuel consumption viewpoint the problem is quite different. Significant reduction of the fuel consumption can be achieved if the variation of the controlled temperature is attenuated [1]. To improve the heating control system efficiency, advanced methods which orient the control system on the building dynamics have to be applied. To compare different types of control algorithms it is necessary to perform many identical experiments. This is, however, almost impossible because of the large number of factors influencing the plant behavior and other difficulties, that are outlined below:  necessity of keeping inhabitants’ thermal comfort which would be strongly disrupted by different experiments. On the other hand the presence of inhabitants is necessary during experiments because their behavior is an important source of disturbances,

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

outer weather conditions (temperature, wind, humidity etc.) strongly influence the plant. Obviously, it is not possible to repeat experiments with exactly the same outer conditions,  the experiment duration should be long enough to observe the heating system performance if different control algorithms are applied. During these experiments the outer conditions can change in much shorter period,  one winter period is usually too short to perform enough number of experiments. Winters conditions are different in succeeding years which cause yet another limitations [2]. The modeling and simulation methods are the best way to tackle the problem. The proper model identification is thus the key issue. The model should explain the plant behavior for input data used during identification but also for all possible disturbances and control signals. The goal of the research is, however, to design a proper control system. The model should then explain relations between most significant disturbances, control and controlled signals. It is thus necessary to determine which disturbances influence the plant the most and which measurements should be involved in the control system. The paper is organized as follows. Section 2 describes data collection and control system used during experiments. All elements of the equipment and dedicated software are presented in this part. Model identification is discussed in section 3. Different methods of non-linear modeling as well as model validation are presented. Analysis of disturbances influence is also described. Section 4 presents control algorithms and strategies tests. In this section simulation results are briefly discussed and general heating control structure is proposed. Section 5 concludes the paper and references are also provided.

2. Data collection and control system To obtain proper model, the identification data must be collected during normal use of the building. Data collection system should not disturb inhabitants’ conditions of living. In the same time it should register as many abnormal situations as possible. To satisfy these requirements, specially designed data collection system is proposed. It consists of two types of units: central unit (CU) and radio modules (RM). General assumptions of the system are as follows:  wire-less communication,  temperature measurements,  digital and analog inputs for non-temperature measurements,  digital and analog outputs to be used by control system,  communication with the PC computer for data storage,  energy-saving work mode,  small dimensions. After careful selection it was decided to design the measurement system on the Atmel microprocessor platform. Fig. 1a presents the CU. It consists of two cards: userdesigned back-side card mostly devoted to allow for the local communication with the user and front-side card (the only part which is visible in Fig. 1a) being the standard Atmel product EB40A with AT91R40008 microprocessor. The CU is equipped with A/D and D/A converters (both with 8 channels of 16 bit), two serial ports RS232, a bus of Dallas digital thermo-elements, 16 digital configurable I/Os, wire-less 433MHz Energy and Buildings 42(9):1510-1516, 2010, doi: 10.1016/j.enbuild.2010.03.021

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communication interface (TeleControlli), 64kB EEPROM, 2MB flash memory, LCD screen with keyboard and additional signalization (LEDs, buzzer, etc.). CU communicates with PC directly through the serial interface or indirectly via the modem and the Internet facility. Using I/O interfaces, the CU can collect data from heating system. It can also buffer data and transfer them out (e.g. to the PC).

(a)

(b) Fig. 1 Central Unit (a) and Radio Module (b) RM unit is presented in Fig. 1b. RM is based on Atmel ATMega8 microprocessor and is equipped with analog outputs, configurable digital I/Os, bus of Dallas digital thermo-elements, wire-less 433MHz communication interface and serial port RS232. The basic role of RM in the system is collecting and transferring data obtained directly from the heating system. It can also transfer any data received via serial port to the wireless network. It is then possible to connect any other measurement device that is equipped with serial port to the wire-less network. Because of its small dimensions and low energy consumption (it can be powered by the 3–5.5V cell) RM can be located in any place of the building. The only problem is the range of the wire-less communication. In the case of too short range a retransmission procedure is applied [3]. To collect desired data it was necessary to introduce additional devices into the system. Vaisala’s indoor humidity transmitter (HMW60) [4] was coupled with RM analog input. Weather conditions are measured with Vaisala WXT 510 transmitter [5]. WXT 510 enables measurements of outdoor temperature, humidity, wind speed and direction as well as the precipitation intensity and duration. The device is equipped with serial port (RS232 or RS485) and was connected with the CU by the wire-less connection via RM. During experiments the general topology of the data collection network was as follows. Four RMs were distributed in the building: two radio modules (RM1 and RM4) were placed in the boiler room and were coupled with CU connected to the PC. They measured temperatures of the boiler (inlet, outlet, etc.), hot water and other temperatures in the boiler room. Binary signals describing state of the heating systems (e.g. state of the boiler, gear of the circulating pump) were coupled with the digital inputs of the RMs as well. RM2 measured temperatures and humidity on the ground floor and RM3 measured temperatures on the first floor. All data from both floors were transmitted to Energy and Buildings 42(9):1510-1516, 2010, doi: 10.1016/j.enbuild.2010.03.021

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the CU in communication with RM1 via wire-less network. RM3 was also used as a gate to the wire-less network for WXT510 device which was installed on the roof. All measured signals and number of sensors are gathered in Tab. 1. The measurements were performed during normal use of the building with existing control system. The plant choice and plant description is presented in section 3. Outer temperature Boiler temperature Hot water temperature Radiator temperature Temperature in the kitchen Temperature in the hall Temperature in the boiler room Other temperatures on ground floor Other temperatures on first floor Indoor humidity Outdoor humidity Air pressure

3 2 4 4 2 1 1 3 3 1 1 1

Wind speed Wind direction Precipitation (rain) Precipitation (hail) Boiler gear Boiler state Room controller output Hot water controller output Pump gear Pump state Thermo-valve set-point

1 1 2 2 1 1 1 1 1 1 1

Tab. 1 Measured signals and number of measurement elements

Fig. 2. Building photography and simple scheme with wireless measurement system

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CU collected data from various devices and sensors. Necessity of data synchronization motivated new software development. The Measurement Server written in Java language collects data from wire-less measurement system every minute. CU retransmits all received packages via serial port to the PC with Measurement Server installed. Collected data are recalculated to the physical units and stored in the data base. The Server also provides synchronization service together with gap-filling based on suitable approximation algorithm. On-line data visualization as well as the historical charts can be also delivered. TCP/IP communication allows for clients connection (in the form of Java Applets) and monitoring of all measured signals via Internet. Of course adequate security mechanisms are applied (user logging, IP filtration, etc.).

3. Model identification 3.1. Building description Improvement of heating control systems is a known problem. There are examples of similar experiments presented in the literature [6,7] but usually specially prepared enclosure and heat sources are used for data collection. In the case described in this paper, the whole building is taken into account which is struggling with realenvironment problems. The first was the choice of the representative building. After careful consideration of all factors influencing thermal properties of the plant a typical detached house of 360 m2 (903 m3) living space was chosen. The building design and construction (see Fig. 2) as well as the used heating system are one of the most popular in the central Europe, where the research was performed. The building walls were constructed with aerated concrete blocks with additional 5 cm of thickness styrofoam insulation. The roof was covered with cement roof tiles and insulated with 20 cm of mineral wool and 5 cm of styrofoam. There are 34 wooden double glassed windows and two doors in the building. The ventilation in the building is natural through 16 ventilation shafts (Tab. 2). Nb i 1 2 3 4 5

Surface type Walls Roof Basement ceiling Doors Windows

Heat transfer coefficient ki [W/m2K] 0.22 0.27 0.68 2.01 1.1

Surface area Ai [m2] 233.81 243.61 82.0 1.8 57.0

Tab. 2 Heat transfer coefficients for different types of the building surfaces. The building was equipped with gas boiler Viadrus G 27 ECO GL with three states (off, 1st step – 27 kW of power and 2nd step – 34 kW), heating system with STELRAD radiators (the water of about 100 l volume is the heating medium) and two-gear pump. The existing control strategy applied in the system was the indoor type with two set points (day and night temperatures) realized in EUROSTER 2000 controller. The boiler was also used to heat a tank with hot water of 300 l volume. There are also two fire places in the ground floor with separate air ducts for heat convection to the first floor. The thermal audit check was executed for building according to [8] and [9] and the validity of the heating system was proven.

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The measurements were collected during normal use of the building so the existing heating control system was on. Then the identification concerned then the system with a feedback. The controller is a non-linear element – the relay with hysteresis which improves conditions of identifiability [10]. The plant itself is also non-linear but smooth enough to make a linear approximation possible. Plant non-linearity follows from the bilinear nature of the heat exchange (difference of temperatures and flow of the heating medium, forced by the circulating pump). It is important to mention that almost all of the signals describing the plant state are continuous. The choice of sampling period and special data preprocessing should be then also considered. Data collection experiment took 6 months of time during two winter periods. All data has been saved in the main PC data base (measured signals are presented in Tab. 1). In the existing control system the temperature in the hall (Th) is the controlled temperature (sensor is located in the most representative place in the building). 3.2. Weather data analysis Measurement system collected large number of different weather conditions parameters (e.g. temperature To, humidity Ho, wind speed Ws and direction Wd, air pressure Pa, precipitation duration and intensity), however, it was decided not to introduce them all into the model. Respective research on weather conditions influence on thermal comfort and fuel consumption was performed and detailed in [11]. Here, only the basic scope of the research and main conclusions will be presented. The research was performed with the use of artificial neural network (ANN). The goal was to evaluate the relative influence of the measured signals on the fuel consumption, indoor temperature and humidity rather than to find the best model. The goal was achieved by comparing the network parameters (weight factors between input neurons and hidden layers) and network response analysis (response for change of only one input signal, while other signals being kept constant and equal to its mean value). The following was assumed:  daily averaged data is used,  data scaled to [0, 1] range to make the comparison possible,  dynamics are included by introducing shifted data samples (one shift for one input neuron),  three different neural networks were examined, one for each output: fuel consumption (N1), indoor temperature (N2), indoor humidity (N3); every network contains one output neuron, one hidden layer of 40 neurons and 10 input neurons: To(i), To(i-1), To(i-2), To(i-3), Ws(i), Ws(i-1), Ws(i-2), Ho(i), Pa(i), Wd(i), where i is the sample number (succeeding days). Each learning process for different sets of data (the same signals from different time periods) was performed until the goal-error of 10-5 is reached. The exemplary N2 network response with real data comparison is presented in Fig. 3a. First step of data analysis was simple hidden layer coefficients comparison. Such comparisons delivered information about relative gain influence of measured signals on the fuel consumption, indoor temperature and indoor humidity. Conclusions from this step allowed for a choice of signals which influence ANN outputs the most. In particular, it was decided to omit the analysis of N3 because from the control point of view it is better to use the measured signal Hi rather than explain it with the model. Another detail concerned analysis of N1. This network responses brought the conclusion, that the air pressure influences significantly the fuel consumption. This follows from the nature of the combustion process and is highly connected with the type of heat source used in the

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system. For example this influence will diminish a lot if the closed combustion chamber is applied. Obviously, the results obtained for air pressure influence might be biased too much because of the heat source used for research. The problem is thus quite interesting and needs additional research possibly with different heat sources applied.

(a) (b) Fig. 3 Teaching process results for N2 (a) and N1 responses in step two (b)

The second step of analysis concerns the ANN responses for different signals changes. The input signals were scaled and the standard deviation of the ANN output was the evaluating factor. For both fuel consumption and indoor temperature the greatest influence obviously comes from the changes of the outdoor temperature. The standard deviation of fuel consumption was varying between 0.112 and 0.153 and the indoor temperature between 0.317 and 0.376 for different sets of scaled data. The influence of wind speed signal was also observed. The standard deviation in this case was varying from 0.092 to 0.103 for N1 (see e.g. Fig. 3b) and from 0.152 to 0.177 for N2. The influence of other signals was substantially smaller. Presented results show that from the control point of view three weather conditions influence the heating system and thermal comfort the most, namely: outdoor temperature, outdoor humidity and wind speed. In the proposed approach presented in section 4 outdoor humidity is replaced with indoor humidity, which is also influenced by other factors (e.g. different activities in the building). For the building with irregular thermal structure the wind direction signal might be also consider to be included especially if the wind speed influences different sides of the building. 3.3. Model structure and parameters The goal of the identification was to obtain a model which can be useful for control algorithms synthesis and simulation. To this end the indoor method of control will be used so the model should explain chosen inner states. This additional degree of freedom simplifies identification procedure. Not all measured data were then used. After assuming some simplifications the following model structure is proposed as in Fig. 4

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To Tk

Kw

Ws Tfp

Kwi

Boiler P

Kk

Radiator

Kb

Qbo

Qbi

Kr

Kfp Qh

Qr

Kh

Th

Heat exchange Re-flow

Khe

Krf

Fig. 4 General structure of the heating system model The general model consists of elementary models (e.g. boiler model, radiators model, etc.) which explains the heat transfer in respective parts of the building. To identify every model separately the positions of thermo-elements was respectively chosen. Firstly, elementary models were identified using least square (LS) method as linear and stationary autoregressive exogenous models (ARX) [12]. Input signals of the elementary models showed in Fig. 4 (To – outdoor temperature, Tk – temperature in the kitchen, Tfp – fire place temperature, Ws – wind speed) are continuous and are not constant between sampling periods thus the identified discrete models were recalculated to the continuous representation using Tustin method [13]. After all elementary models was identified and validated, it was possible to simulate the whole heating system. The first attempt was to use the real input signal P instead of output of the simulated controller. All simulated disturbances have been taken from collected data. Results obtained in the simulations showed that the models were accurate except the hall temperature Th model - see Fig. 5. This model needed improvement.

Fig. 5 Comparison of real and simulated Th signal Energy and Buildings 42(9):1510-1516, 2010, doi: 10.1016/j.enbuild.2010.03.021

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The improvement is possible by using non-linear modeling which follows from interpretation of heat exchange. The time constants of heating and cooling are significantly different. The circulation pump working only when the boiler is ON which enlarges this difference. Therefore for some elementary models (boiler, radiator and reflow) the models have to be doubled – one for heating (boiler is ON) and one for cooling (boiler is OFF). The structure of both models can remain the same – only parameters are different. Non-linearity is realized by parameters change according to P signal changes. This however, caused transients of all models outputs after parameters change. This effect can be hardly eliminated even if special methods of simulation are applied (e.g. smoothing of the parameter changes). Other approach was then the parallel simulation of both models and switching between them similarly to bump-less switching [14]. Consider two state-space models as follows:

 xA  AA x A  B A u   y A  C A x A  DAu

 xB  AB x B  BB u   y B  C B x B  DB u

(1)

The following equality should be fulfilled while switching from “A” model to “B” model at the instant tp

or using (1)

yB t p   y A t p 

(2)

C B x B t p   DB ut p   C A x A t p   DAut p .

(3)

Denoting qt p   C A x A t p   DAut p   DB ut p  one has C B x B t p   qt p .

(4)

Equation (4) does not have unique solution with respect to xB. This fact allows to find solution which fulfils additional requirements, i.e. smooth switching. It is proposed to minimize the states derivatives in the instant tp according to the following index:

J  xB t p  .

(5)

In the case of quadratic norm in (5) the minimizing problem has the form of quadratic programming. The only problem is to keep decision variables of the minimizing problem positive by proper substitution of artificial variables. Fortunately, in the case discussed in this paper time constants differ slightly so it becomes possible to reduce dimensionality of the optimization task only to the dominant state keeping the rest of the states equal to zero during the switching. The switching method smoothed model’s response but still modeling of the Th signal needed improvement. Mismatch shown in Fig. 5 was caused mainly by improper modeling of the outer temperature influence. This problem was solved by using twoinput Hammerstein model [15] which introduces relation between the boiler power and the outer temperature. It should be emphasized that this relation does not exist in the reality. This is an artificial way for modeling of nonlinear behavior of the heating system. The relation is nonlinear and arises from the following interpretation: if the

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boiler is ON only high outer temperature values can disturb the plant; if it is OFF then lower outer temperature values influences the indoor temperature. The idea is then to modify control signal P using measured outer temperature values (Fig. 6a):

P   P'  P  a  To  b T 0 

if if

P  0  To  0 P  0  To  0 if

(6)

P0

where To is averaged outer temperature, a and b are experimentally chosen parameters. The simulation’s results with P signal modification are shown in Fig. 6b. The improvement can be clearly seen when compared with the previous results given in Fig. 5. Similar approach was used in case of wind speed influence which improved the model accuracy as well.

(a) (b) Fig. 6 Modified control signal P (a) and simulation results (b) Model presented above was also expanded with circulating pump gear influence, so the control signal P was changed as well. Instead of P describing state of the boiler and the circulating pump (working on one gear) two input signals were introduced, namely: B – boiler state and Cf – circulation flow. Because of the measurements of outdoor and indoor humidity (Ho and Hi respectively) one more disturbance and one more model output was introduced. As it was explained above relation between Ho and Hi need not to be explained by the model – measurement of the indoor humidity satisfies the control system requirements. To validate the model, simulation experiments for different sets of data were performed. Model inputs were fed with real data collected during two winter periods (outdoor temperature, wind speed, fireplace temperature, temperature in the kitchen etc.). In the first step of validation, the controller signal was also introduced from collected data. In the second step, the simple room temperature controller was simulated as well. The parameters of simulated controller were the same as the real ones. To determine modeling accuracy, real and simulated values of fuel consumption, maximum, minimum and average controlled temperatures in the building were compared. The comparison of simulated and real signals of the boiler inlet, outlet and controlled temperatures were also performed (e.g. Fig. 6b). The fuel consumption was in both cases (real and simulated) calculated by integrating the time when the boiler was

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ON. Tab. 3 presents exemplary comparison of simulated and real data for three different experiments: month, week (from other month), day (from other period then the two first examples). Simulation period Month

Week

Day

Parameter

Real value

Simulated value

Error

Fuel consumption Ti (average) Ti (maximum) Ti (minimum) Fuel consumption Ti (average) Ti (maximum) Ti (minimum) Fuel consumption Ti (average) Ti (maximum) Ti (minimum)

535.68 [m3] 22.53 [oC] 23.44 [oC] 20.38 [oC] 169.35 [m3] 22.03 [oC] 23.53 [oC] 20.04 [oC] 15.83 [m3] 22.21 [oC] 23.11 [oC] 20.15 [oC]

541.73 [m3] 22.41 [oC] 23.13 [oC] 20.95 [oC] 166.43 [m3] 22.17 [oC] 23.22 [oC] 20.71 [oC] 15.99 [m3] 22.43 [oC] 23.34 [oC] 20.45 [oC]

1.1 [%] 0.5 [%] 1.3 [%] 2.8 [%] 1.8 [%] 0.6 [%] 1.3 [%] 3.3 [%] 1 [%] 1 [%] 1 [%] 1.5 [%]

Tab. 3 Exemplary simulation results comparison with real data The results of the model design and identification can be accepted as accurate enough to simulate the heating system. Number of simulation experiments for different lengths of data sets demonstrated that the simulation error did not exceed 2% for fuel consumption calculation. The model explained precisely enough also changes of indoor, boiler inlet and outlet temperatures which are important for more complex control strategies. 4. Control algorithms Model presented in section 3 is accurate enough for researches concerning control algorithms. To compare different control algorithms, adequate quality index has to be chosen. It was decided to compare controlled temperature variation, maximal and minimal values of the temperature, duration time of the overshoots and fuel consumption for the same input data. Many simple and more complex algorithms were tested and compared with the existing on-off room temperature control algorithm. One of them was the standard on-off controller with additional averaging feedback [16]. The other was PD controller with two state outputs. The next two controllers worked in a cascade structure. The additional inner state variable was the boiler output temperature. The inner controller was the standard one. In the first case the outer controller was PI. In the second case it was Generalized Predictive Controller (GPC) [17]. Typical static weather compensation was also compared together with its more complex version – cascade structure with weather compensation. In last two cases outdoor temperature influence was reduced by changing heating medium temperature according to the heating curves. It is worth mentioning that heating curves were identified for the discussed plant. This control method allowed for fuel reduction of about 20% when compared to the existing control algorithm (in simulations). In the same time controlled temperature variations were attenuated with mean value on the same level. All the mentioned results are widely described in [18].

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Although tested algorithms allows for significant fuel reduction they still neglect dynamic properties of the building. Compensation of outdoor temperature is realized in a static way and the reaction for changes is immediate. Such compensation can be too fast, while wall dynamic is significant. As it follows from the results presented in previous section wind speed has also great influence on the fuel consumption. Very important is that all mentioned control algorithms take into account only temperature level rather than the thermal comfort. Thermal comfort is the domain of Heating, Ventilating, and Air Conditioning (HVAC) systems (e.g. [19]). However, the interesting question is if it is possible to assure similar thermal conditions with a standard heating system by introducing more complex control strategies. Instead of ventilating and air conditioning two-layered control system is proposed (Fig. 7).

Fig. 7. General structure of the two-layered control system. Based on the measurements of To, Ws, Ho, and Hi upper layer generates following set points for the lower layer controller: Tis – set point for the indoor temperature, Tbs – set point for the heating medium temperature, Cs – set point for the heating medium flow (forced by the circulating pump), Bp – boiler power and Ap – additional parameters for flow control. Controller in the lower layer generates two control signals: B – boiler control (boiler state and operating power), C – flow control (circulating pump output). Inputs of the controller are Tb – boiler temperature and Ti – indoor temperature signals. The control strategy can be one of the already examined or more complex, e.g. nonlinear predictive control [20]. Compensation of outdoor temperature and wind speed is performed by dynamic nonlinear compensator. Dynamic part of the compensator consists of approximations of control and disturbances paths. Non-linear part is realized as a function of three variables: Tbs  f Tof ,Wsf , Tis  , (7)

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where Tbs is a set point for the boiler output, Tof is the outdoor temperature filtered by the compensator, Wsf is the filtered wind speed and Tis is the indoor temperature set point. The control system corrects the set point of the indoor temperature. This is the only way to improve thermal comfort because existing heating system is equipped with no actuator that could change indoor humidity or air quality. As it follows from the basis of the thermal comfort theory [21] it depends on many variables (e.g. metabolism rate, body surface, inner heat produced by metabolism, latent energy-flux by vapor diffusion, latent energy-flux by sweat evaporation energy-flux by respiration or heat loss by radiation) that are difficult to be measured by the heating control system. Fortunately, many of these variables can be treated as constant and are standardized (e.g. [22]). The indoor temperature set point correction is then performed in the system according to the measured indoor humidity (proper relations can be found in [9]). The proposed twolayered control system was already implemented and tested. Details concerning implementation, experimenting and simulation results are presented in [23].

6. Conclusions The basic difficulty which arose towards new control algorithms testing in real-world experimenting was the long period of experimentation. The proposed method of model identification and simulations proved to be a good aid in such cases. Accurate model allows for testing of different control strategies already implemented in commercial controllers or in CU. For such purpose a dedicated laboratory set-up was developed. PC equipped with AC/DC converters and GeniDAQ software [24] can couple the commercial controller or CU (through its AC/DC converters). The model states (e.g. temperature in the hall, boiler outer temperature) are simulated in the PC as well as all disturbances (e.g. outer temperature, wind speed) are transferred to the CU as the analog signals. This way of CU testing speeds-up experimenting about 300 times if a standard PC is used. Simulation results presented in the paper show that introducing weather parameters as the heating control system inputs allows for the fuel reduction and damps the variation of controlled temperature. It is not necessary to re-arrange the heating system to HVAC in order to keep the thermal comfort in winter period [11]. To improve the system performance further experiments and system development are planned. The research will concern the following issues:  improvement of obtained models; better explanation of weather parameters influence on the plant,  optimization methods for fuel reduction; changing of circulation pump outlet and boiler work parameters can constitute a set of additional control variables,  development of methods for on-line heating surfaces identification as well as the lower layer controller tuning.

6. References [1] Oughton D., Hodkinson S.: Heating and air conditioning of buildings. Butterworth Heinemann, ISBN: 0-7506-4642-X, 2002.

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