**4. Action for energy enhancement**

#### **4.1. Improved control of the regulation system**

As a demonstration of the potential of this model, this paragraph provides an example of the enhanced control system that was integrated in the 2013 scenario. The first scenario was preferred because this baseline is relative to a period during which the heating system was running regularly and assessing to what extent low-cost energy refurbishment action is more expressive because the old heat generator was installed. The assumption made about low-cost actions for energy performance improvement is relative to the installation of an enhanced control system. The community clinic may be equipped with a temperature probe in the most representative rooms, and the rate of the water supply valves might be controlled according to the feedback provided by such records. Two temperature sensors were placed on the ground floor of block A1 and on the second level below the grade of block A2. These two components were connected to two "Real.output" ports, which were then connected to two real input ports within the central plant component (**Figure 9a**). Between of these real inputs and the three-way valves, a hysteresis cluster of components was inserted that sets indoor temperatures between 19 and 21°C. Finally, the new fuel consumption was assessed.

#### **4.2. Discussion of the results**

The modified model 4 was then adjusted and simulated over the 2015 scenario. The results are listed in line no. 6 of **Table 4**. They are still too far from the 5 and 15% thresholds recommended by technical standards. Considering the very disturbed plot for methane gas consumption in this period (**Figure 7b**), changeable water flow rates were assigned to the three pumps. These are expressed as the ratio of the maximum flow rate admissible for each pump: 0.025 of the maximum flow rate until January 15; turned off until January 30; 1.5 of the maximum at the restart (only on January 31); 100% of the maximum rate until February 15; 0.9 of the maximum rate until February 28; 0.02 of the maximum rate until March 31. The last line in **Table 4** shows that the indices in Eqs. (3) and (4) were again constrained within the required limits. This result is even better if we consider the high variability in consumption and in the operational rate of the system in the period under consideration. As a second validation, the hourly temperature plots monitored in the period from February 10 to March 31 were compared with the plots estimated by the Dymola model in the same rooms. **Figure 8** depicts the very good agreement between them. The real and simulated plots relative to "A2 gf" are in very good

**Figure 7.** Comparison between cumulative simulated and measured consumption at the community clinic in 2013 (a)

**Year Scenario Id Stage of the calibration process MBE CVRMSE (monthly)**

−0.19 0.22

−0.05 0.10

−0.04 0.10

−0.05 0.10

−0.04 0.09

0.003 0.13

2013 1 Base model 2013: water flow = {1.0,1.0,1.0}; radiator

2 Modified model 1: water flow = {0.3,0.9,0.4}; radiator

3 Modified model 2: water flow = {0.3,0.9,0.4}; radiator

4 Modified model 3: water flow = {0.3,0.8,0.4}; radiator

5 Modified model 4: water flow = {0.3,0.8,0.4}; radiator

7 Modified model 5: water flow: changeable; radiator

**Table 4.** Analysis of the estimation error on methane gas consumption in the whole building.

2015 6 Base model 2015 0.109 0.28

exp. = {1.24}

exp. = {1.24}

exp. = {1.3}

exp. = {1.24}

exp. = {1.3}

exp. = {1.3}

and 2015 (b).

82 HVAC System

As a result of the simulation performed within the scenario described, the new cumulative fuel consumption of the boiler plotted in **Figure 9b** was calculated. The final value is 18% lower than the benchmark, and such a value represents the average percentage savings that can be obtained each month as a result of the abovementioned enhancement of the control system. This value is strongly dependent on the lowest and highest thresholds set for temperature control in the hysteresis cluster of components and on the control logics chosen

even if relative to low-cost enhancement regarding minor devices of existing subsystems and components. Hence, this software tool may be considered as a valid support for performing

1 Department of Civil and Building Engineering and Architecture (DICEA), Università

2 Department of Civil and Environmental Engineering and Architecture (DICAAR),

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and Emanuela Quaquero<sup>2</sup>

Numerical Approach for the Design of Cost-Effective Renovation of Heating System Control…

http://dx.doi.org/10.5772/intechopen.78613

85

reliable cost–benefit analyses for the energy retrofit of buildings.

\*, Massimo Vaccarini1

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tion. Energy and Buildings. 2008;**40**:394-398

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Brussels: European Commission; 2006

**Author details**

**References**

Alessandro Carbonari<sup>1</sup>

Politecnica delle Marche, Ancona, Italy

Università di Cagliari, Cagliari, Italy

**Figure 9.** The Dymola model relative to the 2013 scenario with enhanced regulation (a) and the energy savings achievable by that renovation solution (b).

for running the heating system. However, the generally valid conclusion is that, once the baseline model has been calibrated for the reference scenario, the benefits deriving from even tailored control policies can be simulated. In the case under analysis, the control subsystem enhancement was relative to a very slight change with respect to the reference scenario. Moreover, it was tailored to the building under analysis, in that only two sensors were placed in two rooms, rather than in every room of the building. Therefore, neither can be classified as a standard control system nor can it be simulated by means of standard whole building analysis tools. In fact, this scenario can be simulated in this model, which is also capable of providing detailed results, such as the temperature difference that this control would generate between floors.

### **5. Conclusions**

In this chapter, the energy model of a typical small public building was developed. It can be considered as representative of a popular category of public buildings that cannot undergo extensive refurbishment because of budget limitations. Hence, the only option is to implement low-cost retrofit solutions that can make energy savings possible by means of reduced investments. This scheme applies to any case in which budget constraints prevent the adoption of complete refurbishment of the audited object. Therefore, in this case, an accurate assessment of potential energy savings is necessary under real dynamic conditions. The case study considered in this chapter was modeled in the Dymola/Modelica environment and was shown to be very flexible, adaptable, and capable of modeling any kind of technology that can reasonably be found in buildings, thanks to its open "Buildings" library. In addition, it was able to provide estimations about the improvements that may be determined by retrofit actions, even if relative to low-cost enhancement regarding minor devices of existing subsystems and components. Hence, this software tool may be considered as a valid support for performing reliable cost–benefit analyses for the energy retrofit of buildings.
