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Decision making for HVAC&R system selection for a typical office building in the UK.

INTRODUCTION

Building energy consumption accounts for more than 38% of the UK energy used in 2009. Besides, HVAC&R systems demand more than 60% of the building energy consumption in the UK (DECC, 2010). This significant energy demand together with the ascending trend in utilizing HVAC&R systems (BSRIA, 2008) underline the importance of selecting the most appropriate HVAC&R system during the design process.

HVAC&R system selection is conducted in the early stages of a design process (Phillips, 2008). Decisions in these early stages significantly influence the total building energy performance and establish up to 90% of the life time occupants' satisfaction and building running costs (Elovitz, 2002). Therefore, "HVAC&R designers are responsible for considering various systems and recommend one or two that will meet the project requirements"(ASHRAE, 2008). However, the lack of tools to investigate a variety of HVAC&R systems and the paucity of systematic selection methods appear to result in to a basic rule of thumb approach to HAVC&R system selection (Ellis and Mathews, 2002; Langmaid, 2004). This, consequently, might result in missing some promising alternatives (Maor and Reddy, 2004).

The open literature reveals two dominant approaches for HVAC&R system selection: 1-Decision making based on the data gathered from simulation studies 2-Decision making based on data collected from large and small-scale surveys. A key investigation using the first approach was conducted by Avgelis and Papadopoulos (2009). Dynamic performance evaluation of HVAC&R systems along with multi-criteria decision making have been utilized to choose the most appropriate system. Simulation of the alternatives using the TRNSYS package along with conducting a formal decision making process using the ElectreIII method are the undeniable strengths of their study. Unfortunately, the investigated systems are limited to few alternatives. This deficiency was addressed through the development of an expert model for HVAC&R system selection in order to automatically synthesize a complete set of possible alternatives (Maor and Reddy, 2004). Despite covering a broad range of alternatives, life-cycle cost was the only criteria used to rank the alternatives.

In the second approach, various studies based on large and small-scale surveys have been undertaken. Shams et al.(1994) developed a knowledge-based model for HVAC&R system selection. Using the same approach, a prototype knowledge-based model (ESCHER) was developed to assist decision makers in HVAC&R system selection (Fazio and Bedard, 1989). Even though capturing the experts' knowledge is the rationale behind the development of the ESCHER model, Shams et al. (1994) demonstrated that in some cases there are many conflicts not only between decision making attitudes of different experts but also between experts and the open literature. These studies were based on capturing the decision makers' attitudes on system selection without analyzing the decision making criteria for system selection. Other researchers attempted to capture HVAC&R system evaluation criteria as well as decision maker's preference from on-site surveys to permit formal decision making (Wang et al., 2009). An advanced Fuzzy decision making method, which has been used by Wang et al. (2009) considers few alternatives and the surveyed results were not clearly described. It seems that the main attempt was to introduce a new application for the advance decision making method rather than to mitigate the complexity of the decision making process for HVAC&R system selection. A review of the existing building energy benchmarks developed using the on-site survey of buildings reveals that the performance of HVAC&R systems are not addressed in detail (Brigges et al., 1992; ECG-19, 2000; CIBSE-TM46, 2008; Torcellini et al., 2008). The investigations show that the survey results do not provide sufficient details to make a clear distinction between surveyed HVAC&R systems. Moreover, in many cases, the root cause of an unexpected performance of a system could not be identified. These two deficiencies have been coarsely overcome by introducing a typical HVAC&R system within energy benchmarks (Brigges et al., 1992; ECG-19, 2000; CIBSE-TM46, 2008; Torcellini et al., 2008).

RESEARCH DESIGN

In this study, the first approach of decision making for HVAC&R system selection is adopted. The decision is based on the data gathered from simulation of the variety of HVAC&R systems for a case study office building. The second approach to decision making is not adopted due to several reasons. These include: 1-The difficulty in capturing the decision makers' attitudes on system selection and the uncertainty in relation to compliance with the open literature (Shams et al., 1994) 2-The difficulty in identifying and capturing the detailed performance of different components of HVAC&R systems in existing buildings (Brigges et al., 1992) 3-There is a concern about the dependency of the performance of HVAC&R systems on building specifications (Korolija et al., 2011).

Among the variety of HVAC&R system simulation tools, Transient System Simulation (TRNSYS) has been selected due to several advantages including thermal comfort analysis (Klein et al., 2009). A formal decision making process is conducted using the outcome of the simulations. In relation to the nature of HVAC&R system selection that is performed in a design and construction environment rather than in a research study, a user-friendly decision making method is always more appropriate. The user-friendly structure of the Analytic Hierarchy Process (AHP) method along with the acknowledged qualities of this method, especially in engineering applications (Yang et al., 2010) are the main reasons for adopting the AHP for decision making in this study (Saaty, 1990).

BUILDING SPECIFICATIONS

Daylit cellular form office buildings account for more than 67% of office buildings in England and Wales (Steadman et al., 2000). Therefore, a daylit cellular office building has been selected for this case study (Figure 1). The glazed area is assumed to be 20% of the floor area (Gakovic, 2000) and the thermal conductance of walls, roof and windows are respectively assumed as 0.28, 0.18 and 1.8 [W/m.sup.2].K (0.049, 0.032 and 0.32 [Btu/hr.ft.sup.2].[degrees]F) (HM Government, 2010a). The infiltration rate is set to 0.3 air changes per hour (CIBSE, 2006). Also, the ventilation rate is assumed to be 10 litres/sec.person (21.2 cfm/person) (HM Government, 2010b). Occupancy density of the prototypical building is considered about 10 [m.sup.2]/person (107.6 [ft.sup.2]/person) (CIBSE, 2006). A power load of 15 [W/m.sup.2] (4.75 [Btu/hr.ft.sup.2] ) is taken into account for lighting energy loads (SLL, 2009). Finally, the electrical equipment load is assumed to be 200 W/person (682 Btu/hr. person ) (CIBSE, 2006). The building is assumed to be in use only during weekdays from 8 am to 6 pm. The indoor temperature is set to 23[degrees]C (73.4 [degrees]F) in cooling mode and 22[degrees]C (71.6 [degrees]F) during heating mode along with 45% relative humidity (CIBSE, 2006). The models of the building and HVAC&R systems are created in TRNSYS and simulated using the London-Gatwick weather data file (Klein et al., 2009).

[FIGURE 1 OMITTED]

HVAC&R SYSTEMS SPECIFICATIONS

In this study, 36 permutations of three primary and 12 secondary systems have been investigated. Tables 1 and 2 illustrate both the primary and secondary systems respectively.
Table 1. Primary systems.

No.  Heating and cooling parts

1    Gas boiler with reciprocating air cooled chiller

2    Gas boiler with absorption chiller and cooling tower

3    Compound heat and power (CHP) with absorption chiller and
     cooling tower (CCHP)

Table 2. Secondary systems.

No.  Part 1               Part 2                  Part 3

1    Constant air volume  --                      --
     (CAV)

2    Variable air volume  --                      --
     (VAV)

3    Constant air volume  Heat recovery (thermal  --
     (CAV)                wheel)

4    Variable air volume  Heat recovery (thermal  --
     (VAV)                wheel)

5    Constant air volume  Economizer              --
     (CAV)

6    Variable air volume  Economizer              --
     (VAV)

7    Constant air volume  --                      Reheat
     (CAV)                                        coil

8    Variable air volume  --                      Reheat
     (VAV)                                        coil

9    Constant air volume  Heat recovery (thermal  Reheat
     (CAV)                wheel)                  coil

10   Variable air volume  Heat recovery (thermal  Reheat
     (VAV)                wheel)                  coil

11   Constant air volume  Economizer              Reheat
     (CAV)                                        coil

12   Variable air volume  Economizer              Reheat
     (VAV)                                        coil


It is assumed that all primary systems are installed in a plant room close to the building and each floor has a separate air- handling unit. The energy efficiency of HVAC&R system components are defined based on the actual manufacturer information within the acceptable range recommended by the recent standards (ASHRAE, 2010).

AHP MULTI-CRITERIA DECISION MAKING

The AHP is a multi-criteria decision making method which is formed based on a pair-wise comparison of decision criteria and alternatives in a hierarchy structure. The hierarchy structure provides an overview of the inherent relation between goal, criteria and alternatives. The AHP method is implemented in practice through five principal steps (Saaty, 1990): 1-structuring the decision hierarchy 2-collecting data and pair-wise comparison 3-checking the consistency of material judgments 4-applying the weighting method to calculate both criteria and alternatives weights 5-aggregating the weights and ranking the alternatives. Figure 2 illustrates the hierarchy structure of the decision making process for HVAC&R system selection used in this study.

[FIGURE 2 OMITTED]

Matrix D, represents the pair-wise comparison of criteria (C) and matrix E, represents the pair-wise comparison of alternatives (A) with respect to each criterion when there are 'n' criteria and alternatives.

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

Pair-wise comparison of criteria and alternatives are measured based on a fundamental nine-point comparison scale, 1: equal importance, 3: moderate importance, 5: strong importance, 7: very strong importance, 9: absolute importance (Saaty, 1990). Then, subjective priority vector (weights of the criteria) and objective priority vectors (weights of the alternatives) are calculated using the Eigenvector Method (Saaty, 2003). The general eigenvector is obtained by perturbation of the following consistent formulation where, W is a priority vector (eigenvector) and the eigenvalue of the matrix E is represented by [[lambda].sub.max].

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

This approach has to be followed to determine the objective priority vectors with respect to each criterion and also to calculate the subjective priority vector (Saaty, 1990). After developing the comparison matrix and compiling both subjective and objective priority vectors (weights), the consistency of the comparison matrix is calculated. The AHP uses a method to evaluate the consistency of a comparison matrix by introducing a Consistency Index (C.I.) and a Consistency Ratio (C.R.). The C.I. of a comparison matrix is given by C.I.=( [[lambda].sub.max]-n)/(n-1) and C.R. is obtained from the ratio of C.I. and the Random Index (R.I.) which were introduced by Saaty (1990).

The approach of the criteria evaluation for each alternative is introduced in this part. First, energy consumption of HVAC&R systems is evaluated using the outcome of simultaneous simulation of HVAC&R systems and the building. It is defined according to the following equation where there are 'j' energy-consuming processes within a HVAC&R system:

TEC = [[SIGMA].sub.1.sup.j]E[C.sub.j] (3)

Also, the energy-related [CO.sub.2] emissions of HVAC&R systems are calculated by considering [CO.sub.2] emission factors of natural gas and electricity, from National Grid equal to 0.19 and 0.55 kg-[CO.sub.2]/kW.h (0.42 and 1.21 lb-[CO.sub.2]/kW.h) respectively (CIBSE-TM46, 2008). It should be remembered that, the main goal of utilizing HVAC&R systems is to provide acceptable indoor environmental quality. The users' satisfaction is measured through the consideration of thermal comfort and indoor air quality (BS/EN:15251, 2007). In this study, an index for deviation from set-point temperature (ITC) is taken into account to measure the level of thermal comfort. With the same approach, an index for indoor air quality (IIAQ) is adopted to measure the level of indoor air quality according to the following equations where there are 'i' spaces which are studied in 'h' hours:

ITC = [[SIGMA].sub.1.sup.i] [[SIGMA].sub.1.sup.h]([DST.sub.i, h] x [X.sub.i, h])/[[SIGMA].sub.1.sup.i] [[SIGMA].sub.1.sup.h] [X.sub.i, h] (4)

IIAQ = [[SIGMA].sub.1.sup.i] [[SIGMA].sub.1.sup.h] ([PPM.sub.i, h] x [X.sub.i, h])/[[SIGMA].sub.1.sup.i] [[SIGMA].sub.1.sup.h] [X.sub.i, h] (5)

Moreover, life-cycle energy costs of the alternatives are calculated based on the net present value (NPV) of money using the following equation (6). In this study, a 20-year life is assumed for HVAC&R systems along with 3.5% discount rate (Churcher, 2008). Also, the initial costs of the main equipment for each alternative are estimated based on the recent cost references (Langdon, 2011).

NPV = C/[(1 + R).sup.N] (6)

DISCUSSION AND RESULTS

To broadly cover the variety of decision makers' attitudes, this study considered six scenarios for subjective priority vectors (weights of the criteria) which are shown in Table 3. The consistency ratio of these priority vectors are also shown in this table. These values are within the acceptable level of 0-0.10 (Saaty, 1990).

After pair-wise comparison of the alternatives with respect to each criterion, the objective priority vectors (weights of the alternatives) are calculated using the Eigenvector method (EM). These values are shown in the left side of Table 4 (columns 4 to 9). Multiply combination of the subjective priority vectors (Table 3) and the objective priority vectors (Table 4, columns 4 to 9) culminates in the final ranking of the alternatives under different scenarios. These results are demonstrated in the right side of Table 4 (columns 10 to 15).
Table 3. Weights of the criteria (subjective priority vectors)
under six different scenarios.

Scenario       Energy  [CO.sub.2]   ITC  IIAQ  Life-cycle  Initial
No.       consumption   emissions                  energy     Cost
                                                     cost

1                0.50        0.10  0.10  0.10        0.10     0.10

2                0.10        0.50  0.10  0.10        0.10     0.10

3                0.10        0.10  0.10  0.10        0.50     0.10

4                0.10        0.10  0.10  0.10        0.10     0.50

5                0.25        0.05  0.30  0.30        0.05     0.05

6                0.05        0.25  0.30  0.30        0.05     0.05

Scenario  Consistency
No.             Ratio

1               0.020

2               0.015

3               0.020

4               0.023

5               0.075

6               0.068

Table 4. Weights (objective priority vector) and final ranks
of the alternatives for different scenarios.

HVAC&R        Alternatives priority vectors with respect to different
System        criteria

No  P  S        Energy  [CO.sub.2]     ITC    IIAQ  Life-cycle  Initial
           consumption   emissions                      energy     Cost
                                                          cost

1   1  1        0.0308      0.0206  0.0222  0.0289      0.0201   0.0505

2   1  2        0.0337      0.0250  0.0239  0.0267      0.0252   0.0363

3   1  3        0.0381      0.0241  0.0230  0.0289      0.0231   0.0350

4   1  4        0.0416      0.0297  0.0297  0.0266      0.0296   0.0276

5   1  5        0.0316      0.0215  0.0226  0.0289      0.0211   0.0411

6   1  6        0.0304      0.0260  0.0256  0.0267      0.0264   0.0312

7   1  7        0.0309      0.0169  0.0332  0.0289      0.0157   0.0418

8   1  8        0.0338      0.0218  0.0337  0.0267      0.0211   0.0316

9   1  9        0.0375      0.0204  0.0335  0.0285      0.0188   0.0306

10  1  10       0.0419      0.0268  0.0338  0.0267      0.0259   0.0248

11  1  11       0.0315      0.0174  0.0334  0.0289      0.0161   0.0352

12  1  12       0.0344      0.0226  0.0335  0.0267      0.0219   0.0277

13  2  1        0.0262      0.0188  0.0222  0.0289      0.0187   0.0459

14  2  2        0.0290      0.0229  0.0239  0.0267      0.0237   0.0339

15  2  3        0.0315      0.0217  0.0230  0.0289      0.0213   0.0327

16  2  4        0.0346      0.0266  0.0295  0.0266      0.0272   0.0261

17  2  5        0.0292      0.0202  0.0226  0.0289      0.0199   0.0380

18  2  6        0.0308      0.0233  0.0256  0.0267      0.0237   0.0294

19  2  7        0.0257      0.0157  0.0321  0.0289      0.0149   0.0386

20  2  8        0.0290      0.0202  0.0337  0.0267      0.0199   0.0298

21  2  9        0.0311      0.0186  0.0322  0.0289      0.0176   0.0289

22  2  10       0.0348      0.0243  0.0337  0.0267      0.0240   0.0236

23  2  11       0.0291      0.0168  0.0321  0.0289      0.0158   0.0329

24  2  12       0.0310      0.0206  0.0336  0.0267      0.0201   0.0262

25  3  1        0.0165      0.0322  0.0217  0.0289      0.0350   0.0200

26  3  2        0.0170      0.0477  0.0232  0.0266      0.0438   0.0173

27  3  3        0.0198      0.0346  0.0222  0.0289      0.0392   0.0170

28  3  4        0.0204      0.0527  0.0240  0.0266      0.0503   0.0150

29  3  5        0.0188      0.0331  0.0219  0.0289      0.0375   0.0187

30  3  6        0.0187      0.0453  0.0235  0.0266      0.0447   0.0160

31  3  7        0.0158      0.0307  0.0308  0.0289      0.0334   0.0185

32  3  8        0.0162      0.0445  0.0263  0.0267      0.0412   0.0162

33  3  9        0.0190      0.0331  0.0306  0.0289      0.0375   0.0159

34  3  10       0.0195      0.0493  0.0263  0.0267      0.0474   0.0142

35  3  11       0.0180      0.0318  0.0310  0.0289      0.0359   0.0170

36  3  12       0.0179      0.0426  0.0263  0.0267      0.0422   0.0151

HVAC&R       Final alternatives Ranks
system      based on scenarios No. 1-6

No  P  S    1   2   3   4   5   6

1   1  1   11  22  23   1  18  31

2   1  2    8  17  17   5  17  28

3   1  3    3  18  18   6  11  26

4   1  4    1   7  11  13   2  10

5   1  5   12  23  25   4  19  32

6   1  6    6  16  16  12  15  25

7   1  7   13  32  32   3   7  19

8   1  8   10  21  24  11   6  16

9   1  9    4  26  26  14   3  13

10  1  10   2  12  14  17   1   8

11  1  11  14  33  34   9   8  20

12  1  12   9  20  21  19   5  14

13  2  1   24  31  31   2  28  36

14  2  2   19  25  20  10  25  34

15  2  3   15  27  27  15  20  33

16  2  4    5  15  15  21   9  15

17  2  5   20  30  30   7  24  35

18  2  6   16  24  22  20  21  30

19  2  7   26  36  36   8  16  29

20  2  8   21  29  28  18  13  22

21  2  9   18  34  33  22  10  23

22  2  10   7  19  19  24   4  12

23  2  11  22  35  35  16  14  27

24  2  12  17  28  29  23  12  21

25  3  1   36  13  12  30  36  24

26  3  2   28   3   4  26  34   3

27  3  3   30   8   7  35  33  17

28  3  4   23   1   1  25  27   1

29  3  5   34  10   9  31  35  18

30  3  6   27   4   3  28  32   5

31  3  7   35  14  13  32  29  11

32  3  8   32   5   6  29  31   4

33  3  9   31   9   8  36  22   7

34  3  10  25   2   2  27  26   2

35  3  11  33  11  10  34  23   9

36  3  12  29   6   5  33  30   6

Note: 'P' and 'S' represent the associated number
of primary system (Table1) and secondary system
(Table2) respectively.


The final ranking of the variety of HVAC&R systems (Table 4) reveals that, when the energy consumption is the dominant criterion (first scenario, Table 3), the reciprocating chiller with gas boiler linked into a VAV air distribution equipped with heat recovery is the best alternative (Table 4). The general trend of the highly ranked alternatives is towards the reciprocation chiller with gas boiler in the primary part and VAV systems in the secondary part. Moreover, when the energy-related [CO.sub.2] emissions are the dominant criterion (second scenario, Table 3), the CCHP linked to a VAV system equipped with heat recovery is seen as the best option (Table 4). Highly ranked alternatives under this scenario are predicted for the systems linked to a CCHP unit. This trend is also observed for the third scenario (dominance of life-cycle energy cost) (Table 3). When the initial cost is the major concern (fourth scenario, Table 3), the reciprocating chiller with boiler linked to a CAV distribution system has the highest ranking (Table 4). Under this scenario, the highly ranked alternatives mostly belong to the systems with reciprocating chillers and gas boilers in the primary part and a CAV distribution system in the secondary part (Table 4). In the fifth scenario (Table 3) when the thermal comfort and indoor air quality have the highest priority followed by a moderate priority for energy consumption, the reciprocating chiller and boiler linked to a VAV system equipped with heat recovery and reheat coil is the top ranked alternative (Table 4). Finally, in the sixth scenario when the indoor air quality and thermal comfort have the highest priority followed by a moderate priority for energy-related [CO.sub.2] emissions (Table 3), the CCHP linked into a VAV air distribution system equipped with heat recovery has the highest rank (Table 4). In general, the highly ranked alternatives under this scenario belong to the systems linked to a CCHP unit. To examine the robustness of the results, a series of sensitivity analyses is performed. The dominant criteria weights are altered between -20% and +20% by taking into account the constant relative weight for the rest of the criteria. The outcomes of the analyses show that the order of the first five highly ranked alternatives is not changed. This highlights the robustness of the HVAC&R system selection approach adopted in this study.

CONCLUSION

This study proposes a robust decision making approach for HVAC&R system selection. Simultaneous dynamic simulation of HVAC&R systems within a prototypical office building has produced a reliable set of information about performance characteristics of a variety of HVAC&R systems. This information along with the associated cost estimations form the basis of the decision making process. Providing six scenarios to represent a variety of different weighted criteria made this study applicable to decision makers with different attitudes. The ranking of the alternatives under the six scenarios shows that the CCHP system, despite the high level of energy demand and also a higher initial investment is highly likely to be selected as a highly ranked HVAC&R system when the energy-related [CO.sub.2] emissions are of importance. Finally, the sensitivity analyses reveal that for all these six scenarios, the order of the first five highly ranked alternatives is not changed. The proposed approach and the results of this study could be used by researchers and designers, especially in the early stages of a design process when all those involved face a lack of time, information and the tools for evaluation of a variety of alternatives in HVAC&R system selection.

ACKNOWLEDGMENTS

Mehdi Shahrestani would like to acknowledge the School of Construction Management and Engineering of the University of Reading for partially funding his doctorate study.

NOMENCLATURE

TEC = Total energy consumption (kW.hr or Btu)

EC = Energy consumption of each process within a HVAC&R system (kW.hr or Btu)

DST = Deviation from the set-point temperature (K or [degrees]F)

ITC = Index for deviation from the set-point temperature(K or [degrees]F)

IIAQ = Index for indoor air quality (ppm)

PPM = Part per million [CO.sub.2] concentration (ppm)

NPV = Net present value ([pounds sterling])

X = Number of people (-)

C = Annual cost ([pounds sterling]/year)

R = Discount rate (-)

N = Year of occurrence (-)

Subscripts

i, h = space 'i' at hour 'h'

j = energy consuming process 'j' within HVAC&R systems

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Mehdi Shahrestani

Student Member ASHRAE

Runming Yao, PhD

Member ASHRAE

Geoffrey K Cook, PhD

Mehdi Shahrestani is a PhD student, Runming Yao and Geoffrey K Cook are readers in the School of Construction Management and Engineering, University of Reading, UK.
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Author:Shahrestani, Mehdi; Yao, Runming; Cook, Geoffrey K.
Publication:ASHRAE Transactions
Article Type:Report
Geographic Code:4EUUK
Date:Jul 1, 2012
Words:4742
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