Energy performance analysis of airport terminal buildings by use of architectural, operational information and benchmark metrics

Journal of Air Transport Management 83 (2020) 101762

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Energy performance analysis of airport terminal buildings by use of architectural, operational information and benchmark metrics Sang-Chul Kim a, Hyun-Ik Shin b, Jonghoon Ahn a, * a b

Hankyong National University, Anseong, South Korea Kumoh National Institute of Technology, Gumi, South Korea

A B S T R A C T

Airport terminal buildings are one of the most energy-intensive building types, and their constituents can include several types of spaces in one-roof structures. However, they have been rarely included in several energy-related studies due to the complexity of their mechanical and operational systems. The purpose of this research is to propose benchmark metrics to investigate the energy performance of existing and future airport terminal buildings. By using measured data of total 30 existing ones in North America and simulated results of total 90 specific space type models, a more improved multivariate regression model can refine the values for average of energy use. When revalued from their characteristics based on the refined average, energy performance forecasts are improved by from 1.1% to 3.0% as compared to the case of the simple average. In addition, it can be used to define a higher energy baseline reflecting some weights adjusted by the building char­ acteristics and to apply it into the space configurations for new airport terminal buildings.

1. Introduction After the College Park Airport in Maryland in 1909, more than 44,000 Airport Terminal Buildings (ATBs) worldwide have been consistently developed to satisfy the rapid growth of the aviation in­ dustry (The Maryland National Capital Park and Planning Commision, 2015; CIA, 2010). Over the 100 years of commercial flight services, numerous changes in engineering, technology, business program, and operational strategy have improved the design and planning related to their functionality and users’ convenience in ATBs. In order to carry out several demands of the services according to environmental specificities, some major aviation agencies and institutions have assisted ATBs to (be) design (ed) as commercial complexes that can meet more intense de­ mands of logistics and transports (TRB, 2010a, b; TRB, 2012; FAA, 2017; ICAO, 2017). However, several systematic and programmatic com­ plexities in ATB design and planning have prevented the quantification of the building’s performance with respect to ATBs unlike office build­ ings, malls, hospitals, and other major building types. This situation is directly related to the difficulty in evaluating ATB’s energy perfor­ mance. For this reason, many researchers and designers have not been able to refer to the energy optimal design of ATBs. In order to define building’s energy performance, most energy ana­ lysts use the concept of the Energy Use Intensity (EUI: kWh/m2-yr or kBtu/sf-yr) to measure energy consumption levels relative to the building’s gross area (AIA, 2012; USEIA, 2008; USDOE, 2013a, b). The

Commercial Buildings Energy Consumption Survey (CBECS) report is typically used to compare the performance as building types for energy benchmarks. According to the report, major commercial buildings are categorized as 14 types: education, food sales, food service, health care, lodging, retail, office, public assembly, public order and safety, religious worship, service, warehouse and storage, other, and vacant (USEIA, 2008). By using over 5000 measured data, the CBECS report indicates EUIs of the 14 building types. For instance, it presents the surveyed results that the EUI of Office Buildings is 293.1 kWh/m2-yr, and Health Care is 592.2 kWh/m2-yr, respectively. However, even though airports, bus stations, train stations, subway stations, and ship berthing facilities are very diverse in terms of the functionalities and operational characteristics, they have not appropri­ ately categorized in details (USDOE, 2013a, b; Lee and Kim, 2017; USDOE, 2013a, b). Among the types, ATBs are one of the largest energy consuming buildings and the largest-scaled building complexes. Some reports and survey results are available to confirm the energy use in­ tensity of some airports at North America (Architecture, 2030, 2012; USEIA, 2008). However, since the number of cases is quite small, it is hard to define appropriate energy consumption levels related to size, business type, number of passengers, and weather conditions (Baxter et al., 2018; Malik, 2017). Even some studies dealing with high refined surveys and advanced performance evaluation models used have not categorized energy issues of ATBs (Chao et al., 2017; Eshtaiwi et al., 2018). Such a situation makes it difficult for researchers to build energy

* Corresponding author. E-mail address: [email protected] (J. Ahn). https://doi.org/10.1016/j.jairtraman.2020.101762 Received 11 March 2019; Received in revised form 2 December 2019; Accepted 3 January 2020 Available online 13 January 2020 0969-6997/© 2020 Elsevier Ltd. All rights reserved.

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Journal of Air Transport Management 83 (2020) 101762

performance guidelines, thus many designers and engineers still do not clearly refer to energy optimal ATBs. Several studies developed energy efficiency measures through the experiments and simulations of build­ ing’s envelope conditions in large areas, and other studies which have dealt with airside facilities, ground power equipment suggested theo­ retical and empirical methods to define air quality with quantification of CO2, SOx, and NOx by and energy consumption of aircrafts by surveys and some prediction models (Cui and Li, 2015; Sukumaran and Sudha­ kar, 2018; Kılkıs and Kılkıs, 2017a, b). In order to construct a method­ ology for synthesizing some data from these sporadic studies, there have been some studies that suggested predictive and deterministic models using subjective surveys or advanced statistical methods (Huang et al., 2015; Baltazar et al., 2018; Wang et al., 2015). Moreover, containing useful analyses about the performance benchmarks for airports have focused on the sustainability of the entire airport system, including a lot of information in various fields such as airside, employment and emis­ sion, so the allocation of ATB’s own planning issues have not been very large (Postorino and Mantecchini, 2014; Kilkis & Kilkis, 2016; Kilkis & Kilkis, 2017a, b). Studies that have constructed a methodology for improving the energy efficiency of the HVAC system by highlighting the relationship between the characteristics of space planning and passen­ gers, or that have revealed actual commissioning results through the analysis of actual results specifications of airport-specific HVAC systems, provide very useful information. However, they have some weaknesses to generalize because most studies are limited to specific conditions of specific airports (De Rubeis et al., 2016; Liu et al., 2019; Sukumaran and Sudhakar, 2018; Postorino and Mantecchini, 2014). Therefore, it is necessary to develop an energy benchmark metric to investigate the level of energy performance of ATBs. After ATBs have played a commercially important role, many energy-related studies analyzed buildings’ performance and gathered measured data to estimate their energy end use. Despite such efforts, there has been still a lack of guidelines or manuals for energy efficient buildings. Even though the growing demands of energy efficient ATBs, useful energy performance metrics have not been proposed either. In the future, as well as commercial purposes, it will become clear that more complex systems will be required in terms of safety and security. The existing benchmark research of corresponding energy consumption levels will not be able to meet the levels required by future increasing demands. Although there have been insightful references for the air transport business and its characteristics, it is also true that there have been no appropriate intersection with studies in the field of under­ standing the various aspects of airport buildings that have been actually used for important purposes. Therefore, a number of studies on air transport management need to be linked to the management of build­ ings, thereby expanding existing achievements. This paper presents an intuitive energy benchmark metric for ATBs using both actual data and simulated energy consumption results to reduce possible errors derived from their small number of ATB samples. Section 2 summarizes energy performance indicators and benchmark tools dealing with effective factors and metrics, and proposes an inte­ grated regression model. In Section 3, the proposed model is tested with actual measured data, and shows the result. In Sections 4 and 5, the model’s effectiveness, practical implications and a follow-up study are discussed.

Fig. 1. Flow diagram for methodology.

ASHRAE, 2014). Benchmark models have developed a regression model utilizing various elements of building geometries, climate conditions, Heating, Ventilation, and Air-Conditioning (HVAC) systems, and oper­ ational strategies. Among them, a clear equation model has been preferred by most energy analysts (Monts and Blissett, 1982; Sharp, 1996; Rajagopalan and Leung, 2012; Ahn et al., 2016; Cho, Ray, Im, Honari, & Ahn, 2017, 2019). EUI ¼ a þ b1 x1 þ b2 x2 þ ⋯ þ bk þ xk þ ε

(1)

where a is an intercept, b1 …bk are regression coefficients, x1 …x1 are standardized values, and ε is a random error. Standardized values of each factor are calculated as non-unit values: Z ¼ ðX

μÞ = σ

(2)

where, X is a value of the variable, μ is mean, and σ is standard deviation. By using the idea, the EUI of buildings can be easily calculated, and the EUIs of various buildings can be compared. In order to define the effectiveness of the models, some statistical tools are required. To find the amount of variations or dispersion of results derived from models, the standard deviation is typically used, and to find delicate differences between models or correlations between factors, the regression analysis and the Analysis Of Variance (ANOVA) test can be used. In the linear regression analysis, the Root Mean Squared Error (RMSE) measures the average of the squares of the errors or deviations, the R-squared (or adjusted R-squared) values indicate how much of the total variation in the dependent variable can be described by the independent variable (Agresti and Finlay, 1999; Lund Research Ltd, 2018). � R squared R2 (3) The Sum of Squares of Residuals �1 The Total Sum of Squares sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn 2 y t yt Þ t¼1 ðb RMSE ¼ n

2. Methodology

(4)

where, n is population, ybt is independent (predicted) values, yt is re­ gression’s dependent variable. When normalizing by the mean value of the measurements, the term Coefficient of Variation (CV) of the RMSE, may be used to avoid ambi­ guity (UCLA, 2016). This approach is similar to the CV with the RMSD taking the place of the standard deviation (UCLA, 2016). Eq. (5) in­ dicates the CV of RMSE.

The energy benchmark is a process of accounting for and comparing a metered building’s current energy performance with its energy base­ line, or with the energy performance of different building types. Fig. 1 which describes the process for benchmarking was used to compare the performance over time, within and between peer groups, or to document top performers. Moreover, the references outlined minimum data input in benchmark systems such as building characteristics and energy con­ sumption levels (EISA Section 432, 2010; APEC, 1999; Stroud, 2015; 2

S.-C. Kim et al.

CV of RMSE ¼

Journal of Air Transport Management 83 (2020) 101762

RMSE b y

Table 1 Space types and share of the floor spaces.

(5)

where, b y is mean of the dependent variable. As indicated, the value of R-squared is increasing as the sum of squares of residuals is decreasing, whereas the value of RMSE is decreasing as the mean square error is decreasing. In other words, the correlation between dependent and independent variables is increasing as the value of R-squared is increasing, which is equivalent to the fact that models which have higher values of R-squared, or lower values of RMSE or Standard Error of the estimate (SE) are statistically precise. The regression analysis provides ANOVA table which consists of F ratio (F) and Significance (Sig.). the value of F is defined by dividing mean square between groups by mean square within-groups, so models which have large F ratio are relatively effective (Elvers, 2013; Agresti and Finlay, 1999). The value of Sig. (or p-value) from ANOVA test is the significance of the F ratio. If the Sig. is less than or equal the α-level, then the null assumption (H0) that all the means are equal can be rejected. Typically, if the Sig. is greater than 0.05, it is failed to reject H0, which means that there is insufficient evidence to claim that some of the means may be different from each other (Elvers, 2013). In other words, if the Sig. is less than 0.05, the regression model significantly predicts the outcome variable. The EUI of ATBs can be affected by various factors such as area, age, lighting, climate condition, enplanement, and other conditions. Among them, several factors are specified by building types in ATBs, but other factors are specified by the characteristics of the entire ATB such as enplanement. Eq. (6) is rewritten pursuant to this idea: EUIAdj ¼ EUIMea þ ½ðSum of All Impacts in ATBÞ þðSum of Other Impacts of ATBÞ� ¼ EUIMea þ ½c1 y1 þ c2 y2 þ … þ ck yk þ ε1 � þ ½j1 z þ j2 z2 þ … þ jk zk þ εk �

No.

Building (space) type by Stantec

Building (space) type by CBECS

Share of Space

1 2

Office Space Concessions - Retail, Food

15% 5%

3

Baggage Handling & Screening

4

General Passenger Areas (Check-in, Arrivals, Waiting areas, Washrooms, etc.) Vacant Space (Currently Unoccupied) Additional Areas (Corridors, Storage, etc.)

Office Retail, Food Service Public Order & Safety Public Assembly Other Other

2% 40%

5 6 Total

10% 28%

100%

where, ‘ω9 ’ is weighted value as share of the total space. All weighted values, regression coefficients, and standardized values from the calculation process are assigned at right positions. Then refined EUIAvg;Adj derived from all values of EUIAdj is obtained. All weighted values, regression coefficients, and standardized values from the calculation process are assigned at right positions. Through the comparisons between the values of EUIMea , EUIAdj , and EUIEst , the en­ ergy performance levels of all target ATBs are evaluated, and the effectiveness of the improved metric is verified. 3. Results Typically, ATBs consist of multiple building (space) types in one structure, such as Office, Retail, Mall, Food Service (FS), Public Order and Safety (PO&S), Public Assembly (PA), and other Support Areas. Energy consumption patterns of ATBs are complicated due to the complexity of space types and operational characteristics. The two re­ ports provided the EUIs of 10 and 12 actual ATBs in North and Central America, respectively. According to the reports, Ronald Reagan Wash­ ington National Airport consumed about 1009.4 kWh/m2-yr, but Salt Lake City Int’l Airport consumed 473.2 kWh/m2-yr. Dallas Fort Worth Int’l Airport consumed 561.5 kWh/m2-yr, while Los Angeles World Airport consumed 649.8 kWh/m2-yr (CAP, 2003; Stantec, 2012). Although the reports did not provide clear patterns of energy con­ sumption, it can be concluded that the EUI variations are due to factors such as building geometries, building (space) types, operation, and each ATB’s business model. Additionally, the composition of ATB’s building (space) types should be analyzed to define their characteristics.

(6)

where, ‘EUIMea ’ is actual measured EUI as an intercept, ‘ck ’ and ‘jk ’ are regression coefficients, ‘yk ’ and ‘zk ’ are standardized values, and ‘ε1 ’ and ‘ε9 ’ are random errors. The data that can be used to benchmark the energy consumption of ATBs are derived from the Stantec and Clean Airport Partnership (CAP) reports. Stantec, an architectural service company in Edmonton, pro­ vides geometry data and energy consumption levels of 12 airports in North and Central America to develop a reporting procedure for quan­ tifying baseline energy usage intensity and greenhouse gas emission. The US Department of Energy released its 10 airport survey in North America including energy use, policies, and operational programs by and with the efforts of the CAP. Based on the Stantec report (following referrals to this research are based on this reference hereafter), the ATB space designated as office and concession are 15% (0.15) and 5% (0.05), respectively. It is assumed that the percentage for concession is divided as 2.5% (0.025) and 2.5% (0.025) for retail and food service, respec­ tively. It is assumed that energy consumption patterns of public order & safety, public assembly, and others areas are proportionally reflected in the EUIMea, so they are neglected in this model. Thus, the values of EUIAdj from the improved metric are obtained:

3.1. Weighted values and regression coefficients The Transportation Research Board (TRB), which is a program unit of the National Academy of Sciences, Engineering and Medicine to provide research-based solutions to improve transportation, summa­ rized the building (space) types of ATBs: ticketing/check-in, passenger screening, baggage claim/handling, holdroom (departure lounge), concession, office/operation area, support area, and circulation (TRB,

EUIAdj ¼ EUIMea þ ωk ½ðSum of All Impacts of Office in ATBÞ þ ðSum of All Impacts of Retail in ATBÞ þ ðSum of All Impacts of FS in ATB� (7)

þ ½Sum of Other Impacts of ATB� EUIEst ¼average of each value of EUIAdj þ ωk ½ðSum of All Impacts of Office in ATBÞþðSum of All Impacts of Retail in ATBÞþðSum of All Impacts of FS in ATB� þ ½Sum of Other Impacts of ATB�

(8)

3

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Table 2 Database of 20 ATBs from the CAP and Stantec reports. No.

Ref.

Airport Name

State/ Country

Climate Condition Zone

HDD65

CDD50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Stantec Report (2012)

SF Int’l Toronto Pearson Salt Lake City Calgary Int’l Dallas Fort Worth Int’l Kamloops Nassau Int’l Sangster Int’l Hamilton Cranbrook Fort St. Johns LA World Hartsfield Jackson Atlanta Int’l Seattle-Tacoma Int’l Fort Lauderdale-Hollywood Int’l Ronald Reagan Nat’l Portland Int’l Cleveland Hopkins Int’l Pittsburgh Int’l Cincinnati-Northern Kentucky Int’l

CA CAN UT CAN TX CAN BAH JAM CAN CAN CAN CA GA WA FL VA OR OH PA KY

4 6 4 7 2 5 1 1 6 6 7 3 3 4 1 4 6 5 5 4

2758 7677 5342 9576 2327 6707 148 30 7677 8578 10,782 1194 2764 4641 138 4895 7661 6145 5908 5260

2901 2021 3366 1134 6413 2293 9616 9904 2021 1378 947 4704 5279 2021 9748 3672 1929 3042 2923 3514

CAP Report (2003)

2010a, b; Haberl et al., 2015; Turner et al., 2007). Stantec’s definition of ATB building (space) types of 12 airport projects include office, concession retail and food service, baggage handling and screening, general passenger area, vacant space, and additional area. As shown in Table 1, ATB’s space types are defined by the information available from the Stantec, the TRB, and the CBECS reports. In the case of using the CBECS reports, this study uses previous version in order to utilization of related surveys and studies because it has been the most widely used in the field of energy benchmarking for now, and it effectively shows the trend of energy use in each space type when the CAP and the Stantec reports were created. This aspect provides a strong hint of follow-up studies to be mentioned in the end of this study. In the CBECS report, office, retail, and food service building types are filtered by the following explanatory variables: building area (Area), building age (Age), number of business (Business), number of occupants (Occupant), Heating Degree Days (HDD), and Cooling Degree Days (CDD). From the 27 variables, Offices, Area, HDD, and CDD are used to define the significance. As indicated in Table 2, the information of 20 ATBs in the CAP and Stantec reports are used to analyze the impacts of Area, Enplanement, HDD, and CDD. Through some samples not so many compared to other common commercial buildings, the benchmark pro­ cesses can provide an effective guideline for energy efficient ATBs, when existing ATBs’ construction and operational information is not available to find a suitable case for new ATBs. Through the ACRP web-only document 27 in late 2015, the TRB released the benchmark results of 10 ATBs and their raw data surveyed. The information provided includes geometry, share of the floor space,

Size

Area (m2)

Enplanement (2011)

EUI (kWh/m2yr)

L L L M M M M M S S S S L L L M M M L L

4,79,364 3,39,550 1,00,332 1,42,416 2,06,888 1,69,635 22,946 39,297 8500 3159 2787 1951 2,83,874 2,32,250 83,695 49,942 1,42,481 87,119 1,69,558 1,78,275

2,00,56,568 1,20,71,857 97,01,756 64,32,675 2,75,18,358 56,45,934 15,50,000 15,00,000 1,66,330 1,31,645 64,268 55,000 4,44,14,121 1,59,71,676 1,13,32,466 90,53,004 68,08,486 44,01,033 40,70,614 34,22,466

542.57 435.32 593.05 801.24 561.50 495.26 359.61 520.49 463.71 599.36 615.13 649.83 459.20 735.12 573.24 1010.39 512.76 692.98 939.03 733.99

climate conditions, operational characteristics, and other useful details. The 10 ATBs are tested through the MRM and the energy performances are analyzed. Table 3 organizes the information from the ACRP docu­ ment by the Transportation Research Board (TRB), and the data sheet by the National Academies of Sciences, Engineering, and Medicine (NASEM). However, measured data include unexpected outliers and missing data. To validate and complement them, 90 simulations by EnergyPlus are performed. Table 4 indicates the simulation configurations such as templates and parameters used. The EnergyPlus simulation algorithm includes more precise parameters and values in templates. Measured data can be refined by theoretical results from the simulation to reduce physical errors, outliers, and other missing data. The factors are then standardized to remove the effect of deviance from each different scale. Finally, by the standardized values of specific factors (as data on the X axis) and their EUIs (as data on the Y axis), 40 sets of table and scatter plot are obtained. Fig. 2 describes the EUI dis­ tribution for measured data and the EnergyPlus simulation. The wide range of variances of data points is seen, so significance validation is required. Some points in Fig. 2, such as 2179.8 kWh/m2-yr and 1772.8 kWh/m2-yr, could affect the result as a noticeable outlier. A statistical validation process is required to nullify the effect of outliers and clarify the significance. For further study, a more rigorous statistical method could be applied to reduce the effects of outliers. The effect of enplanement can be larger than the area because the absolute value of the regression coefficients is about two times larger than the area. However, the data distribution of enplanement shows an unclear

Table 3 Database of 10 ATBs from the ACRP and the NASEM documents. No.

No. in ACRP

Size

Climate Zone

1 2 3 4 5 6 7 8 9 10

#1 #2 #3 #4 #5 #6 #7 #8 #9 #10

Large Medium Nonhub Large Large Large Medium Small Large Small

2 2 2 3 3 4 4 5 6 6

ATB Floor Area (m2)

Age (2014)

Total

Office

Retail

Food Service

1,38,196 1,18,351 4827 2,24,527 2,67,919 1,53,822 3,65,995 67,093 4,63,202 25,275

86,041 60,464 2990 1,23,217 1,94,881 80,411 1,49,690 35,709 3,02,616 11,505

13,889 10,140 351 25,942 25,746 8666 36,575 4621 32,680 2220

1341 592 0 2724 11,469 0 7790 901 14,661 165

4

2634 2612 50 6255 903 2208 7204 718 4642 852

Degree Days HDD

CDD

1435 981 1625 3138 2327 5049 7661 6089 7677 7477

7215 7485 6747 4823 6413 3816 1929 3405 2021 2237

EUI (kWh/m2-yr) 470.69 573.62 473.02 534.01 402.88 560.60 716.52 550.61 448.56 643.81

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measured data of the total area and enplanement are required to more accurately define statistical validation. As some examples of factors shown in Fig. 2, the corresponding regerssion coefficients are common to all ATBs in the benchmark model. The p-values between simulated scenarios and the CBECS display relatively large differences. Therefore, more precise data collection is required to define these differences. FS in CBECS trends differently compared to office and retail. This result can explain why FS may have a different business model in terms of number of customers, lighting, and equipment. Various factors affect the energy consumption level of commercial buildings. Through the analysis of the regression co­ efficients and Sig., eight factors are defined to be applied into [Sum of All Impacts] in the improved metric.

Table 4 Simulation conditions and configurations. No.

Parameters

Detail

1 2 3 4 5 6 7 8 9 10 11

Building Type Location Area # of Stories Building Age # of Business # of Occupants Lighting Equipment HDD CDD

Office, Retail, Food Service 2009 IECC 7 Climate Zones (random) 5000–50,000sf (random selection) 1–5 (random selection) 4–22 (based on EnergyPlus sets) 1–6 (random) EnergyPlus default template EnergyPlus default template EnergyPlus default template USDOE weather data USDOE weather data

tendency as compared to the area. In general, higher CDDs and HDDs contribute to EUI increases in buildings. However, due to higher temperature differences between outdoor and indoor room temperature, heating requires more energy than cooling in most climate zones. For instance, in summer, cooling energy operates to decrease temperature from over 30 � C to 25� C-28 � C, but in winter, heating energy operates to increase temperature from under 10 � C to 20� C-25 � C. This difference makes the heating load more critical than the cooling load in most zones with four distinct seasons. In the case of office and retail areas, the values of EUI increase as the values of HDD increase in simulated and CBECS data. Because heating energy

3.2. Case study From the process, the EUIAdj of Dallas-Fort Worth Int’l Airport (DFW) is calculated. In Table 6, EUI, geometry, climate condition, and enplanement from the FAA database are actual measured data, but other details follow the assumptions derived from the projected results by the share of floor space in the Stantec report and the linear regression of the CBECS data (FAA, 2015; Stantec, 2012; USEIA, 2008). By using Eq. (8), the Standardized (Std.) Values, and design param­ eters, the final EUIAdj are obtained:

EUIAdj ¼ average EUI þωOffice *½ðRC � area *Std:Valuearea�Þ þ ð RCHDD *Std:ValueHDD Þ�þ ð RCCDD *Std:ValueCDD Þ� þ ωRetail�* RCage *Std:Valueage þ ð RCHDD *Std:ValueHDD � Þ � þ ωFS * ðRCarea *Std:Valuearea Þ þ RCage *Std:Valueage þ ð RCCDD *Std:ValueCDD Þ ¼ 178:00 þ0:15*½ð 4:0088Þ*ð5:2704Þ þ ð6:6995Þ*ð 1:0750Þ þð 4:5057Þ*ð5:3680Þ� þ0:025*½ð23:9460Þ*ð3:2483Þ þ ð15:6150Þ*ð 0:9570Þ� þ0:025*½ð 61:1340Þ*ð2:3508Þ þð 29:2485Þ*ð0:3630Þ þð22:6470Þ*ð3:5297Þ� ¼ 169:37 kBtu=sf :yr ¼ 534:31 kWh=m2:yr

consumption directly affects EUI by increasing it, the regression co­ efficients of office and retail areas display the larger positive number. The FS shows the insignificant negative number of the regression co­ efficients, which may be caused by factors specific to FS, such as kitchen and dining area equipment as heating sources. As the values of CDD of office and retail increase, the values of EUI decrease. Table 5 describes the results of RCs and Sig. of the relationship be­ tween EUIs and factors, and parts of the values were reported (Ahn et al., 2016). The p-values less than 0.05 show a strong relationship between the EUI and the factor in the ANOVA test. The Sig. of the number of businesses and the number of occupants describe that there are no sig­ nificant relationships between them and changes in EUI. At this point, it can be expected, as the area increases, the energy use itself increases. If the area is 100 and the energy use is 100, it can be expected that, in the case that the area is 200, the energy use can be 200. However, in this case, the two EUIs are equal to 1 and 1. This is what this paper find through this statistical process. The basic assumption is that the energy use is expected to increase if the number of users increases, but it will not necessarily lead to an increase in the EUI. Therefore, the two factors can be neglected as impact factors in the improved metric. The total area and enplanement in 20 ATBs are not confirmed as significant factors, so they can be neglected in the improved metric. For further study, additional

(10)

3.3. Comparison of three EUIs Based on the calculation process, 20 ATBs in the CAP and Stantec reports and 10 ATBs in the ACRP report are analyzed. Tables 7 and 8 summarize the results. In the cases of No. 1, 2, 13, and 14 in Table 7, the differences between the conventional and the improved metrics are relatively larger than the others. Those ATBs are categorized as large-scaled ATB, but there are no intersections about building’s age and climate zone. The building size in ATBs can correlate with measures of energy consumption, but no other factors seem to be correlated significantly with a tendency toward changes in EUI. As indicated, it can be confirmed that the EUIAdj of SFO is 507.2 kWh/m2-yr, and the Diff. between the EUIMea and EUIAdj is over 35 kWh/m2-yr (near 7%). Through the removing impacts of unstan­ dardized factors, the EUIMea is normalized. This result implies the fact that SFO is being operated inefficiently if space planning and opera­ tional characteristics are normally designed. In the cases of #5 and #7 in Table 8, the results describe relatively poor performance to EUIEst from the improved metric. In addition, the results of average of Diff. are derived from offsetting sum of all impacts 5

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Journal of Air Transport Management 83 (2020) 101762

Fig. 2. (a) EUI vs. Area for 30 simulated office buildings; (b) EUI vs. Age for 30 simulated retails; (c) EUI vs. HDD for 355 retails in the CBECS; (d) EUI vs. Enplanement for 20 ATBs in the CAP and Stantec.

during the calculation of values of EUIEst. For these reasons, the simple comparison of differences is not enough to intuitively clarify the effec­ tiveness of the improved metric and to identify numerical comparison.

effectiveness to estimate energy performance of each or future ATB. In the case of 20 ATBs from the CAP and the Stantec reports, both models for EUIAdj are confirmed as an effective measure in ANOVA test. However, the values of RMSE, R-squared, and F ratio verify the metric’s performance. For EUIEst, the values from ANOVA test confirm that both models do not have statistically strong relationship with EUIMea. The CV of RMSE defines the fact that the metric for EUIAdj and EUIEst is improved as much as 7.8% points and about 1.1% points, respectively. In the case of total 30 ATBs, the effectiveness of both models is not changed much as compared to the result of 20 ATBs. However, the relationship between EUIMea and EUIEst become quite stronger than the case of 20 ATBs. This implies a fact that more ATBs samples increase the effectiveness of the improved metric. The CV of RMSE defines the fact that the metric for EUIAdj and EUIEst is improved from 11.5% to 3.2% and from 27.7% to 24.7%, respectively. In brief, the conventional model have been used do not precisely reflect real world, but the improved metric reduces the unstandardized impacts derived from space planning and layout. In the cases of EUIAdj, the application of weighted values in the metric contributes to the reduction of unstandardized effects derived from unstandardized space planning. It is confirmed that the application of the metric provides higher efficiency in calculating both values of EUIAdj and EUIEst. Weighted values can play an important role to reduce errors in calcu­ lations of EUIAdj, whereas the effectiveness of the metric can be defined by the application of the EUIAvg,Adj during the projections of EUIEst.

4. Discussion To verify the effectiveness of the improved metric in comparison with conventional model, some statistical validation processes are per­ formed: Comparison of Maximum (Max) and Minimum (Min) of Diff.; Regression analysis including RMSE, R-squared, SE, and ANOVA test. Table 9 describes the statistical results of 4 different EUI indicators. All values in regression and ANOVA are derived from the linear regression analysis reflecting values of EUIMea as dependent variables and EUIAdj and EUIEst as independent variables. Lower values of ‘RMSE’ and ‘SE, and higher values of ‘R-squared’ and ‘F ratio’’ indicate that the regres­ sion of the model used shows a similar (or close) pattern in the observed data. Less values than 0.05 of ‘Sig.’ indicate statistically strong re­ lationships between some of the means. 4.1. Effectiveness of the improved metric As indicated in Table 9, most values of the metric confirm that the improved metric show better performance. Especially, the effectiveness of EUIAdj is considerably improved by about 72% for 20 ATBs and 77% for 30 ATBs, respectively. Although difference of two averages is rela­ tively small (about 0.5%), the improved metric indicates a little of 6

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Journal of Air Transport Management 83 (2020) 101762

Table 5 RC and Sig. (p-value) as factors and building types.

Table 7 Results of EUI measures for 20 ATBs.

No

Factor

Building n Type

Data Type

RC

p-value

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

Area

Office

Sim. CBECS Sim. CBECS Sim. CBECS

¡4.0088 3.1546 1.8542 0.0890 3.7834 ¡61.1340 8.5526 3.6753 0.1381 0.6221 23.9460 6.8020 18.2420 ¡76.7390 2.6727 2.5285 0.8611 0.2790 2.8694 31.0270 3.1300 2.0159 3.4855 3.0260 6.8344 6.5645 17.5090 13.7210 0.7885 21.2060 15.1330 4.6768 1.0246 ¡4.5057 4.5149 2.8092 22.6470 12.0670 15.8050 7.1413

0.041 0.068 0.750 0.984 0.666 0.001 0.395 0.757 0.946 0.719 0.000 0.114 0.028 0.000 0.183 0.146 0.883 0.948 0.740 0.093 0.117 0.729 0.687 0.799 0.000 0.000 0.001 0.001 0.927 0.252 0.127 0.694 0.615 0.009 0.436 0.513 0.005 0.515 0.110 0.546

Retail FS 27 Offices 22 Airports Office

Age

Retail FS Business

Office Retail FS

Occupant Enp HDD

Office Retail FS 22 Airports Office Retail FS 27 Offices 22 Airports Office

CDD

Retail FS 27 Offices 22 Airports

Sim. CBECS Sim. CBECS Sim. CBECS Sim. CBECS Sim. CBECS Sim. CBECS Sim. Sim. Sim. Sim. CBECS Sim. CBECS Sim. CBECS Sim. CBECS Sim. CBECS Sim. CBECS

No.

1

San Francisco Int’l 2 Toronto Pearson 3 Salt Lake City Int’l 4 Calgary Int’l 5 Dallas Fort Worth Int’l 6 Kamloops 7 Nassau Int’l 8 Sangster Int’l 9 Hamilton 10 Cranbrook 11 Fort St. Johns 12 Los Angeles World 13 Hartsfield Jackson Int’l 14 Seattle-Tacoma Int’l 15 Fort LauderdaleHollywood Int’l 16 Ronald Reagan Washington National 17 Portland Int’l 18 Cleveland Hopkins Int’l 19 Pittsburgh Int’l 20 CincinnatiNorthern Kentucky Int’l Average

Table 6 Variables. Parameters

Detail

1 2

Name Climate conditions

3 4

Age Total floor area Office

Dallas-Fort Worth Int’l Airport Climate zone 2 HDD65: 2327 CDD65: 2,2759 39 206,895.1 m2 31,034.3 m2 (Electricity for lighting: 391.7 kW) (Number of equipment: 1251.26) 5172.4 m2 (Electricity for lighting: 132.0 kW) (Number of equipment: 24.40) 5172.4 m2 (Electricity for lighting: 94.0 kW) (Number of equipment: 13.63) 2,75,18,358 561.5 kWh/m2-yr

Retail Food Service 5 6

Enplanement EUI

EUI Mea (kWh/m2yr)

EUI Adj

542.57

507.23

435.32 593.05

kWh/ m2-yr

EUI Est Diff. (%)

kWh/ m2-yr

Diff. (%)

6.5

566.35

4.4

414.64 580.99

4.8 2.0

581.01 589.64

33.5 0.6

801.24 561.50

793.51 534.29

1.0 4.8

593.96 574.47

25.9 2.3

495.26 359.61 520.49 463.71 599.36 615.13 649.83

481.35 343.55 499.47 470.96 611.48 630.01 646.90

2.8 4.5 4.0 1.6 2.0 2.4 0.5

587.78 585.63 580.66 608.93 613.81 616.57 598.76

18.7 62.8 11.6 31.3 2.4 0.2 7.9

459.20

430.41

6.3

572.90

24.8

735.12

715.15

2.7

581.71

573.24

546.23

4.7

574.68

1010.39

1002.57

0.8

593.87

41.2

512.76 692.98

501.34 683.53

2.2 1.4

590.26 592.24

15.1 14.5

939.03 733.99

923.28 716.81

1.7 2.3

585.94 584.51

37.6 20.4

614.69

601.68

2.1

588.68

4.2

20.9 0.3

Table 8 Results of EUI measures for 10 ATBs. No.

No.

Name

No. of ACRP/ Size/Climate Zone

1 #1/Large/2 2 #2/Medium/2 3 #3/Nonhub/2 4 #4/Large/3 5 #5/Large/3 6 #6/Large/4 7 #7/Medium/4 8 #8/Small/5 9 #9/Large/6 10 #10/Small/6 Average

EUI Mea (kWh/m2yr)

EUI Adj kWh/ m2-yr

Diff. (%)

EUI Est kWh/ m2-yr

Diff. (%)

470.69 573.62 473.02 534.01 402.88 560.60 716.52 550.61 448.56 643.81 537.43

451.99 552.15 467.08 492.68 383.15 555.51 690.24 554.13 444.03 651.89 524.29

4.0 3.7 1.3 7.7 4.9 0.9 3.7 0.6 1.0 1.3 2.4

498.50 495.73 511.25 475.87 497.47 512.11 490.92 520.71 512.66 525.29 504.05

5.9 13.6 8.1 10.9 23.5 8.6 31.5 5.4 14.3 18.4 6.2

In Fig. 3, the impact values of office buildings and food services are quite large negative values in comparison with retails. In other words, the space planning or operational characteristics of the two space types for existing 30 ATBs are not optimized. If floor space for office buildings is decreased or envelope retrofitting for food services is performed, the impact values are increased. Consequently, this process induces an in­ crease of EUIAdj, and the difference between EUIMea and EUIAdj is decreased, which means the fact that actual energy consumption ap­ proaches theoretical and optimized energy consumption. Referring regression coefficients and p-values in Table 5, more effective and intuitive strategies to improve energy performance for existing 30 ATBs is to: 1) Decrease floor space of office or food service; 2) Retrofit building envelopes of retail or food service; 3) Upgrade heating system of office or retail, and cooling system of office or food service.

4.2. Impact of space types From Eqs. (8) and (9), impacts of space types are confirmed. Fig. 3 indicates the average impact values of each space type for 30 ATBs. Negative values decrease the value of EUIAdj, and positive values in­ crease the value of EUIAdj. This result implies the fact that the space types which have negative impact values are not designed and being operated energy efficiently. 7

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Journal of Air Transport Management 83 (2020) 101762

Table 9 Comparison of the models for ATBs. ATBs

Statistics

20 ATBs in CAP and Stantec

Absolute of Difference Regression

ANOVA 30 ATBs in CAP, Stantec, and ACRP

Absolute of Difference Regression

ANOVA

For EUIAdj Max (%) Min (%) RMSE CV of RMSE (%) R-squared SE of the Estimate F ratio Significance (p-value) Max (%) Min (%) RMSE CV of RMSE (%) R-squared SE of the Estimate F ratio Significance (p-value)

For EUIEst

Conv. Model

MvRM

Conv. Model

MvRM

30.6 0.1 64.5 10.8 0.87 60.30 125 0.000 30.6 0.1 66.0 11.5 0.84 19.32 144 0.000

6.5 0.5 18.3 3.0 0.99 13.28 2936 0.000 7.7 0.5 18.7 3.2 0.99 4.30 3447 0.000

73.5 0.4 170.9 28.6 0.00 169.90 0.044 0.837 73.5 0.4 158.2 27.7 0.01 47.72 0.279 1.899

70.1 0.2 160.0 27.5 0.02 168.19 0.413 0.529 70.1 0.1 143.0 24.7 0.06 46.45 1.843 0.584

(RMSE, SE of the Estimate: kWh/m2-yr).

advanced statistical techniques will be utilized to reveal possible hidden interactions between each factor, and more comprehensive surveys with the latest version of the CBECS reports will be performed to reduce statistical errors as follow-up studies. 5. Conclusion This research examined the intersection of energy end uses, energy benchmarks, commercial buildings, and ATBs to achieve effective met­ rics which can evaluate and estimate the energy performance at any phases in building design. In order to complement some existing energy benchmarks, a multivariate regression model combining actual data and simulation results is suggested and tested to define its effectiveness. The proposed metric has three different approaches: 1) It presents an improved approach to remove unstandardized factors and outliers by adding weighted values and cross-checks between measured and simu­ lated data. Through data trimming and filtering, it enables users to easily compare actual and theoretical energy performance of ATBs; 2) Despite of a sudden change in building’s elements, there is no need to re-model or re-simulate to account for the impact of the changes on other ele­ ments; 3) It presents the possibility to investigate energy performance of other mixed-use buildings. In circumstances that may require any new building space types, these methods will be very helpful to make quick decisions in early design phase for future ATBs. Consequently, by utilizing both actual measured data and theoretical simulation results, the proposed model can provide users several op­ portunities for decision making within the design phases. Moreover, the examination can be a starting point to expand the usability of the model with different building types, building clusters, and urban scale. Thus, for transportation systems, which are the most important part of smart cities, and for plans to improve the eco-friendly of the aviation sector that will lead to the connection between such smart cities, the meth­ odological study of these benchmark metrics could do much to support rapid decision-making in establishing strategies for improving energy efficiency in the early design stages for ATBs. The method of this research is based primarily on the architectural features and environmental factors of ATBs. However, in addition to these physical characteristics in an actual aviation business, operational specificities are a large part of the airport’s effectiveness to cope with a variety of real-time changing situations, such as management strategies, national transport policies, and international economies. A follow-up study will incorporate elements from this operational perspective to investigate more adaptive energy benchmark models. As a follow-up study, a comprehensive analysis will be performed with combining the existing and the latest versions to investigate some changes in trends of

Fig. 3. Comparison of performance impacts as space types.

4.3. Practical implications The improved benchmark metric used in the study reveals the weaknesses of the conventional methods and some opportunities in terms of the possible practical implications. First, the methodology presents an improved way to define energy efficient design baselines of ATBs when there are not enough measured data. Through measured and simulated results by trimming and filtering, it enables users to easily compare existing and future energy performance of ATBs. Second, as indicated in Tables 2 and 3, the in­ crease of energy use does not mean the increase of EUI. Therefore, data collection from all kinds of architectural, mechanical, and operational aspects is required, especially to analyze them by use of the share of the indoor space. In this case, the method of this study can save resources to investigate energy patterns of each space in all cases by defining re­ lationships through the statistical analyses of measured data and simu­ lation results. Third, it presents the possibility of the energy performance benchmark for other mixed-use space in future ATBs. By inputting the specific building’s geometry and space planning, quick decisions for energy efficiency strategies can be made at various phases in the future ATB design. However, there is a clear limitation that this metric does not reflect the spaces for connecting the airside and landside, and the vehicle sys­ tems which are not included in the architectural studies. Especially, the energy consumption analysis for the passenger embarking and dis­ embarking gate extensions is hard to investigate, because it is difficult to standardize energy consumption pattern per space area as it is close to a machine in the airside. In order to improve model’s validation, some 8

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buildings’ energy performance to incorporate various elements from the operational perspective in more recent air transport businesses. In addition, some advanced statistics such as artificial neural network and machine learning technologies will be used to find possible hidden in­ teractions between the factors of architecture and transport manage­ ment and to quantify the validity of this benchmarking process.

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