Energy performance optimisation of building envelope retrofit through integrated orthogonal arrays with data envelopment analysis

Journal Pre-proof Energy performance optimisation of building envelope retrofit through integrated orthogonal arrays with data envelopment analysis

Hong Xian Li, Yan Li, Boya Jiang, Limao Zhang, Xianguo Wu, Jingyi Lin PII:

S0960-1481(19)31640-4

DOI:

https://doi.org/10.1016/j.renene.2019.10.143

Reference:

RENE 12512

To appear in:

Renewable Energy

Received Date:

27 June 2019

Accepted Date:

25 October 2019

Please cite this article as: Hong Xian Li, Yan Li, Boya Jiang, Limao Zhang, Xianguo Wu, Jingyi Lin, Energy performance optimisation of building envelope retrofit through integrated orthogonal arrays with data envelopment analysis, Renewable Energy (2019), https://doi.org/10.1016/j.renene. 2019.10.143

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

Journal Pre-proof

Energy performance optimisation of building envelope retrofit through integrated orthogonal arrays with data envelopment analysis Hong Xian Li1, Yan Li1, Boya Jiang2, Limao Zhang3,*, Xianguo Wu4, Jingyi Lin4 1. School of Architecture and Built Environment, Deakin University, Locked Bag 20001, Geelong, Victoria 3220, Australia. E-mail: [email protected]; [email protected] 2. School of Architecture, Nanjing Tech University, Nanjing 211816, China. E-mail: [email protected] 3. School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore. E-mail: [email protected] 4. School of Civil Engineering & Mechanics, Huazhong University of Science and Technology, Wuhan Hubei 430074, China. E-mail: [email protected]; [email protected] * Corresponding author: [email protected] (L. Zhang)

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Journal Pre-proof Abstract: Retrofitting building envelopes is regarded as an effective solution that can help commercial and individual investors offset daily power usage. However, it is worthwhile to explore a highly efficient approach to seek optimum retrofitting strategies. A novel hybrid approach that integrates energy simulation, Orthogonal Array Testing (OAT), and Data Envelopment Analysis (DEA) is developed in this research to discover optimal solutions for building retrofit. A commercial high-rise building is chosen as a case study, and five parameters are considered, including the exterior envelope fabric, exterior window type, sunshade type, window-to-wall ratio, and airtightness. The energy consumption is first simulated and verified as a baseline. OAT is then employed to conduct experiments and explore potential solutions to the energy optimisation problem, based on which the most efficient strategy is obtained through DEA benchmarking. The identified optimal solution is able to save an annual operation energy of 7.01 kWh/m2, which is also cost-effective. It is also found that the window type and airtightness are significant factors with regard to the energy performance of building envelope retrofit. The study benefits designers and construction managers in determining the optimal solution of retrofitting building envelope for achieving energy-efficient building operations. Keywords: Building envelope retrofit; Energy optimisation; Orthogonal Array Testing; Data Envelopment Analysis; Energy simulation

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1. Introduction

2

The building sector accounts for approximately 30% of global energy consumption and more than

3

50% of global electricity demand [1]. As for commercial buildings, more than 80% of building energy

4

consumption occurs during the operation phase to maintain indoor environments and provide building-

5

based services [2, 3]. Thus, building energy consumption, especially for commercial buildings, can be

6

considered one of the major contributors to such issues as the depletion of fossil fuel reserves and

7

environmental pollution [4, 5]. To promote energy-efficient building operations, great efforts have

8

been made in search of active or passive efficiency strategies for building energy [6-8]. Active

9

strategies aim to meet the human requirements for comfortable environments using electrical-

10

mechanical equipment, whereas passive strategies optimise and conserve the usage of potential energy

11

by buildings prior to the electrical energy conversion [9]. The active strategies primarily focus on the

12

improvement of heating, ventilation, and air conditioning (HVAC) systems, heat pumps, boilers, and

13

electrical lighting. However, active technologies are confronted by some insurmountable limitations

14

[6]. For example, the coefficient of performance for a heat pump is normally no greater than 6. Passive

15

energy-saving technologies have been widely exploited in recent decades [10], and they are mainly

16

involved in the improvement of building envelope elements, such as Trombe walls [11], lightweight

17

concrete walls and slab [12], and green roofs [13].

18

A building envelope aims to physically separate the indoor from outdoor environments of a

19

building for the purpose of resistance to air, water, light, and heat [14, 15]. A large amount of heat

20

exchange is achieved during the building operation phase. The heat can be transferred into and

21

maintained within indoor environments, creating an ‘oven’ effect in hot climates, but indoor

22

temperature can decrease because of heat losses. Both under-insulated and over-insulated envelopes

23

may cause an increase in loads of cooling and heating. Thus, the building envelope significantly affects

24

energy efficiency and indoor environmental quality [16, 17]. Chung et al. [18] observed 31.4% in

25

energy savings for the selected high-rise apartments in Hong Kong when extruded polystyrene thermal

26

insulation was added to walls. Balaras et al. [19] indicated that energy consumption for insulated

27

buildings may be 20%–40% less than in non-insulated buildings, and for low infiltration buildings

28

may be up to 20% less than in high infiltration buildings in Greece. Consequently, researchers and

29

engineers have realised that building energy performance can be improved by means of the optimal

30

design of the building envelope. For instance, Lin et al. [20] demonstrated that the optimal office

31

building envelope configuration could save approximately 40% of the energy consumption. Braulio-

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Gonzalo and Bovea [21] optimised a building’s envelope insulation thickness in order to balance the

33

environmental and cost performance of the building.

34

In recent years, several optimisation models have been developed to assist building designers in

35

search of optimal design solutions when coupled with building energy simulation theories [22, 23].

36

Different design aspects of the building envelope have been extensively explored in order to achieve

37

energy-efficient goals since the first introduction of the simulation-based optimisation methods [24,

38

25]. For instance, Evins [26] and Nguyen et al. [27] reviewed simulation-based optimisation methods

39

adopted in the building performance analysis and design. Wu et al. [28] proposed an optimisation

40

model for building energy systems in typical residential buildings in the Swiss village of Zernez, where

41

the optimization model was integrated into the dynamic energy simulation in the EnergyPlus platform

42

to explore individual retrofit scenarios. However, Huang and Niu [29] criticised in a literature review

43

that a number of previous studies on simulation-based building envelope optimisation utilised a single

44

factor for the optimal solution for the minimisation of energy consumption.

45

Indeed, practical systems usually involve three or more variables or factors, particularly for the

46

complex building energy systems, which requires multi-factor analysis on a simulation platform [30].

47

Several factors, such as the window-to-wall ratio (WWR), thermal insulation, glazing types, and roof

48

strategies, contribute to building energy performance. For instance, Capeluto and Ochoa [31]

49

conducted a simulation-based study to identify and rank energy-efficient retrofitting solutions in 13

50

urban centers and identified that the thermal insulation and glazing had the most significant impact on

51

energy consumption reduction in Central Europe. Raji et al. [32] strongly recommended four measures

52

on improving glazing types, WWR, sun shading, and roof strategies for improving the energy

53

performance of the envelopes of high-rise office buildings in the Netherlands. It can be concluded that

54

the optimal designs of the building envelope with energy simulation technologies require multi-factor

55

analysis. However, based on the previous research, it is worthwhile to explore the following issues:

56

(1) How to ensure the validity of the energy simulation model for the complex building envelop

57

systems? (2) How to design fractional factorial experiments to cover the most important features of

58

the problem studied? (3) How to discover the optimal strategy through benchmarking activities in the

59

multi-factor analysis? It is, therefore, necessary to develop a highly efficient approach to deal with the

60

optimisation of multiple factors with different levels of values.

61

Orthogonal Array Testing (OAT) is designed for studying multi-factors and multi-level

62

experiments, aiming to identify the representative cases for lowering the number of test cases [33, 34].

63

This method requires a relatively small number of trials and less time to acquire an optimal level group

64

of Decision Making Units (DMUs). It allows for performing both range analysis and variance analysis

65

to evaluate test results. There is an increasing interest in utilising the orthogonal array experimental 4

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method for optimisation in various fields [35, 36]. Furthermore, Data Envelopment Analysis (DEA) is

67

a powerful performance measurement and benchmarking tool for applications, especially when the

68

evaluated DMUs are represented by activities representing real processes that generate products [37].

69

DEA provides each DMU with an efficiency score that has to be viewed as its relative efficiency in

70

the set of all DMUs involved in the benchmarking [38]. DEA is derived from the economic notion of

71

Pareto optimality, which is a nonparametric method in operational research and has been applied to

72

various fields [39, 40]. The first mathematical model proposed by Charnes et al. [41] was called DEA

73

Charnes Cooper and Rhodes (CCR) with a constant return to scale. Banker et al. [42] later extended

74

this basic model to a case with variable rate to scale, namely DEA Banker Charnes and Cooper (BCC).

75

As a powerful tool for measuring the productive efficiency of DMUs, DEA proves to be time-efficient

76

in length of optimisation analysis and suitable for input–output variables with different units [43].

77

Moreover, DEA is capable of accurately and effectively identifying new DMUs and reaching an

78

optimal solution once the search space is reduced by using OAT [44]. Currently, the algorithm

79

integrating OAT with DEA [44] has not yet been widely used. In particular, there has been little

80

research on searching for optimal envelope retrofit strategies based on the building energy performance

81

simulation through the combination method.

82

Therefore, a novel hybrid approach that integrates the computer simulation, OAT, and DEA is

83

established to address the above-mentioned three issues and the purpose of this research. The

84

integrated method is capable of lowering a number of scenarios through OAT and seeking the most

85

efficient solution through DEA on the simulation platform. In this research, a high-rise building is

86

chosen as a case study and modelled on a reliable energy simulation platform in order to optimise the

87

primary factors of building envelope for improving the energy performance of commercial high-rise

88

buildings. First, the simulation is implemented to predict the energy performance of buildings. The

89

simulation results are then validated by the monitored data. Finally, the OAT strategy is adopted for

90

the identification of more efficient scenarios and pre-assessment of the search space of the energy

91

simulation optimisation problem. DEA is employed to rank the efficient scenarios within the new range

92

and seek for the optimal retrofit solution for improving the energy performance of high-rise buildings.

93

The optimisation approach can be beneficial for decision-makers to minimise the energy consumption

94

of high-rise buildings in envelope design and retrofit projects.

95

The remainder of the paper is organised as follows: Section 2 presents the developed systematic

96

methodology with detailed step-by-step procedures and proposes a verified energy simulation model.

97

Section 3 selects a realistic high-rise building as a case study to demonstrate the optimal strategy.

98

Section 4 aims to present the simulation results and validation. Section 5 proposes the OAT

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optimisation; and Section 6 identifies the optimum retrofit strategy through DEA. Section 7 proposes

100

the conclusions and future work.

101

2. Methodology

102

In order to conduct the multi-factor energy analysis and optimisation in complex building systems,

103

a novel hybrid approach that integrates the computer simulation, OAT, and DEA is developed in this

104

research. This approach is capable of performing various what-if scenario analyses and discover the

105

optimal strategy for energy savings. Figure 1 illustrates the flowchart of the developed hybrid approach

106

for energy simulation and optimisation. Three main steps are incorporated, as elaborated below. Dynamic Energy Simulation and Validation

OAT Optimisation

• DesignBuilder • Design parameters • Model development • Mean Bias Error • Mean Square Error • Tolerance range

DEA Benchmarking • Input-output setup • Decision units • Relative efficiency • Comparsion

• OAT experiments • Variance analysis • Total deviation • Total freedom

107 108 109

Figure 1. Flowchart of the developed hybrid approach for energy simulation and optimisation. 2.1 Dynamic energy simulation and validation

110

The first step aims to consider an appropriate platform for establishing a simulation model. In

111

order to achieve accurate dynamic calculation, energy simulation software tools have been extensively

112

developed by engineers and researchers, such as DOE-2, Ecotect, DeST, EnergyPlus, and

113

DesignBuilder [45]. Table 1 summarises the characteristics of the above-mentioned simulation tools.

114

It can be concluded that DesignBuilder is capable of investigating more detailed consumption

115

situations and providing a user-friendly graphical interface by comparison. Thus, DesignBuilder is

116

chosen to simulate and analyse the energy consumption of commercial high-rise buildings in this

117

research.

118 119

Table 1. Comparison of building energy simulation tools. Characteristics

Building Energy Simulation Tools DOE-2

Ecotect

DeST

EnergyPlus

DesignBuilder

Graphical interface

√

√

√

─

√

Simulation accuracy level

Hour

Hour

Hour

Minute

Minute

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Auto-check data rationality

√

√

√

─

√

Accurate temperature display

─

─

√

√

√

Natural ventilation

─

─

√

√

√

Wall moisture transfer

─

─

─

√

√

Thermal comfort calculation

─

─

─

√

√

Sunshade and lighting control √

√

√

√

√

Economic analysis

─

√

√

─

─

Customised output report

─

─

─

√

√

Note: “√” represents feasibility; “─” represents unfeasibility.

121 122

DesignBuilder operates by using EnergyPlus as a simulation engine and considering the

123

interaction of all the building components and systems such as building envelope, windows, HVAC,

124

and internal heat gain from different systems. The heat balance can be simplified as follows: 𝑁

125

𝑁

𝑁

∑𝑖 =𝑠𝑙 1𝑄𝑖 + ∑𝑖 =𝑠𝑢𝑟𝑓𝑎𝑐𝑒𝑠 𝑄𝑠𝑖 + ∑𝑖 =𝑧𝑜𝑛𝑒𝑠 𝑄 + 𝑄𝑖𝑛𝑓 + 𝑄𝑠𝑦𝑠 = 0 1 1 𝑧𝑖

(1)

𝑁

126

in which ∑𝑖 =𝑠𝑙 1𝑄𝑖 represents the total internal loads. 𝑁𝑠𝑙 indicates the number of convective internal

127

loads, 𝑄𝑖. The convective heat transfer 𝑄𝑠𝑖 can also be expressed as ℎ𝑖𝐴𝑖(𝑇𝑠𝑖 ― 𝑇𝑧), which is the

128

convective heat transfer from the ith surface at temperature 𝑇𝑠𝑖 of the zone air at a temperature, 𝑇𝑧

129

[46]. 𝑄𝑧𝑖, namely 𝑚𝑖𝐶𝑝(𝑇𝑧𝑖 ― 𝑇𝑧), represents the heat transfer owing to inter-zone air mixing, and 𝑄𝑖𝑛𝑓,

130

namely 𝑚𝑖𝑛𝑓𝐶𝑝(𝑇∞ ― 𝑇𝑧), denotes the heat transfer owing to infiltration, as described in Engineering

131

Reference [47]. 𝑄𝑠𝑦𝑠 represents the total heat flow of a building system. The air heat balance [47] in

132

DesignBuilder can be formulated as 𝑑𝑇𝑧

𝑁

𝑁

𝑁

133

𝐶𝑧 𝑑𝑡 = ∑𝑖 =𝑠𝑙 1𝑄𝑖 + ∑𝑖 =𝑠𝑢𝑟𝑓𝑎𝑐𝑒𝑠 ℎ𝑖𝐴𝑖(𝑇𝑠𝑖 ― 𝑇𝑧) + ∑𝑖 =𝑧𝑜𝑛𝑒𝑠 𝑚𝑖𝐶𝑝(𝑇𝑧𝑖 ― 𝑇𝑧) + 𝑚𝑖𝑛𝑓𝐶𝑝(𝑇∞ ― 𝑇𝑧) + 𝑄𝑠𝑦𝑠 1 1

134

(2)

135

The rate of energy storage in air (W) is written as 𝐶𝑧 𝑑𝑡 , in which the capacitance, 𝐶𝑧, takes the

136

contribution of the zone air into account [16].

𝑑𝑇𝑧

137

The commonly used verification method is to prove the simulation accuracy according to the

138

requirement of the specification. For energy conservation evaluation, the American Society of Heating,

139

Refrigerating and Air-Conditioning Engineers (ASHRAE) Guideline 14-2002 [48] is considered a

140

widely-accepted building energy model calibration standard [49]. It provides both the individual

141

system and the whole building calibration procedures. Similar to the whole building calibration

142

simulation, the calibration process adopts the computer simulation to establish the building model prior 7

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to the energy-saving retrofit, which is compared with the actual energy consumption in operation to

144

calibrate the model. The model is then employed to forecast the energy savings of the building after

145

the retrofit. The error indicators in ASHRAE Guideline 14-2002 are selected as judgment standards of

146

the model verification. The two dimensionless indicators are Normalised Mean Bias Error (NMBE)

147

and Cumulative Variation of Root Mean Square Error (CVRMSE), as formulated in Eqs. (3) and (4),

148

respectively. 𝑃

149

𝑁𝑀𝐵𝐸 =

150

𝐶𝑉𝑅𝑀𝑆𝐸 =

[

∑𝑝 = 1(𝑆𝐸𝑝 ― 𝐴𝐸𝑝) (𝑃 ― 1) × 𝐴𝐸

2 𝑃 ∑𝑝 = 1(𝑆𝐸𝑝 ― 𝐴𝐸𝑝) (𝑃 ― 1)

]

× 100

(3)

12

× 100 𝐴𝐸

(4)

151

where 𝑆𝐸𝑝 and 𝐴𝐸𝑝are simulated and actual energy consumption values of the month p, respectively;

152

𝐴𝐸 is the average value of the actual energy consumption.

153

Clearly, the indicators consider both the actual and the simulated energy consumption data, which

154

can be obtained by the monthly energy bills and simulation models, respectively. The ASHRAE

155

Guideline defines that the simulation models are reliable when -5% ≤ NMBE ≤ 5% and CVRMSE ≤

156

15%.

157

2.2 OAT-enabled optimisation

158

As mentioned earlier, OAT is considered to be a highly-efficient approach for achieving the

159

optimisation of multiple factors with different levels of values. It can significantly reduce the workload

160

resulting from the increase in the number of factors and levels of values. This step aims to analyse the

161

effects of envelope-related parameters on building energy consumption. The energy-saving potential

162

could be generated by thermal design parameters of building envelope from a sensitivity perspective.

163

OAT is used to arrange and test the performance of the proposed optimisation strategies and further

164

explore the feasible region of the energy optimisation problem. The orthogonal table is the foundation

165

of the orthogonal experimental design, which forms as follows:

166

𝐿𝐷(𝑄𝑀)

(5)

167

𝐿 represents the symbol of orthogonal design, 𝐷 denotes the number of rows or tests, 𝑄 indicates the

168

number of levels, and 𝑀 represents the number of columns or factors [50].

169

Both the range analysis and variance analysis can be performed based on the test results. The

170

range analysis aims to measure and demonstrate the impact range of each factor using the difference

171

between the maximum and minimum mean values of test results. The results can be further analysed

172

through the variance analysis to identify effects from experimental conditions, errors, and the

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importance of factors. For the statistics of the F distribution, the value of F is compared to a critical

174

value of a significant level, which is normally set at 0.05 or 0.01.

175

The impact of the selected factor on the test results is considered to be significant if it is greater

176

than the critical value, and vice versa. Setting 𝑦𝑑 as the output result generated by the dth orthogonal

177

test, the square sum of the total deviation, 𝑆𝑇, and the degrees of the total freedom, 𝑑𝑓𝑇, can be

178

expressed as Eqs. (6) and (7), respectively, in the variance analysis. Herein, for the mth factor, the

179

square sum of the deviation, 𝑆𝑚, and the freedom degrees, 𝑑𝑓𝑚, are expressed as Eqs. (8) and (9). 1

𝐷

(

𝑆𝑇 = ∑𝑑 = 1𝑦𝑑2 ― 𝐷 ∑𝑑 = 1𝑦𝑑

181

𝑑𝑓𝑇 = 𝐷 ― 1 1

(6) (7)

1

𝑄

2

)

𝐷

180

(

𝐷

2

)

𝑆𝑚 = 𝐷 𝑄∑𝑞 = 1𝐷𝑞𝑚2 ― 𝐷 ∑𝑑 = 1𝑦𝑑

182

(8)

183

𝑑𝑓𝑚 = 𝑄 ― 1

(9)

184

where, 𝐷𝑞𝑚 represents the sum of the test results of Factor m on Level q. For errors, the square sum of

185

the deviation, 𝑆𝑒, and the freedom degrees, 𝑑𝑓𝑒, are defined as Eqs. (10) and (11), respectively.

186

𝑆𝑒 = 𝑆𝑇 ― ∑𝑑𝑆𝑚

(10)

187

𝑑𝑓𝑒 = 𝑑𝑓𝑇 ― ∑𝑑𝑓𝑚

(11)

188

where the value of F is defined as the ratio of the mean square error (MSE) for factors to the MSE for

189

the deviation. MSE is equal to the ratio of the square sum of deviation to the freedom degrees.

190

2.3 DEA benchmarking

191

DEA is proven to be a powerful tool that can be used to perform a comparison of the relative

192

efficiencies of DMUs. The production possibility set, 𝑃𝐵, of the DEA BCC model was defined by

193

Banker et al. [42].

194

𝑃𝐵 = {(𝑥,𝑦)|𝑥 ≥ 𝑋𝜆, 𝑦 ≥ 𝑌𝜆,𝑒𝜆 = 1,𝜆 ≥ 0}

(12)

195

𝑋 and 𝑌 represent the vectors of input and output variables, respectively. 𝜆 indicates a column vector

196

with all elements non-negative. 𝑒𝜆 = 1 denotes the adjunction of the condition in BCC models, where

197

𝑒 is a row vector with all elements of unity. The step aims to rank the efficient DMUs through DEA

198

benchmarking, seeking the most efficient strategy and adopting the optimal building envelope. The

199

number of tests, 𝐷, is significantly reduced to 𝐷' through the orthogonal array testing, which means

200

that 𝐷' is the total number of DMUs in DEA. The DEA BCC model can be expressed as follows.

201

𝐽

max 𝑤0 = ∑𝑗 = 1𝑢𝑗𝑦𝑗0 + 𝑐0

9

(13)

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{

𝐽

𝐼

∑𝑗 = 1𝑢𝑗𝑦𝑗𝑑 ― ∑𝑖 = 1𝑣𝑖𝑥𝑖𝑑 + 𝑐0 ≤ 0 (𝑑 = 1, 2, …, 𝐷') 𝐼

∑𝑖 = 1𝑣𝑖𝑥𝑖0 = 1 𝐼

subject to: ∑𝑖 = 1𝑣𝑖𝑥𝑖0 = 1 𝑢𝑗 ≥ 0 (𝑗 = 1, 2, …, 𝐽) 𝑣𝑖 ≥ 0 (𝑖 = 1, 2, …,𝐼) 𝑐0: unrestricted

202

(14)

203

where 𝑑, 𝑖, and 𝑗 represent the DMU, input, and output index, respectively. Their corresponding total

204

numbers are 𝐷', 𝐼, and 𝐽, respectively. 𝑥𝑖𝑑 and 𝑦𝑗𝑑 indicate the ith input and jth output for the dth DMU,

205

respectively. 𝑣𝑖 and 𝑢𝑗 represent weights of input and output, respectively. 𝑤0, 𝑥𝑖0, and 𝑦0 denote the

206

relative efficiency, input, and output for DMU0, which is the DMU under evaluation. 𝑐0 represents the

207

free variable.

208

3. Case Study

209

For the preliminary application of the research approach proposed in the previous section, an

210

existing high-rise building in Wuhan, a city located in Central China, is chosen as a case study to

211

optimise the energy-saving performance. The three-dimensional (3D) simulation model is constructed

212

on the DesignBuilder platform, as shown in Figure 2. The selected high-rise is a mixed-use commercial

213

building that includes office, business, restaurant, and conference facilities. The area of the building’s

214

lot measures approximately 1,700 m2; the building’s total floor area is 34,650 m2; and the height of

215

the building is 92 m, which includes 25 floors. The ground floor provides a lobby and a business center

216

with a height of 6.0 m. The first floor has a dining area with a height of 4.8 m. The 25th floor features

217

a conference center integrated with a multi-functional hall along with water tanks and equipment

218

rooms; the height of this floor is 6.3 m. Other floors have office areas the height of which are 3.4 m.

219

The area of the building envelope is 16,124 m2, and the building volume is 126,198 m3, resulting in a

220

shape coefficient of 0.13 and a WWR of 0.3.

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Figure 2. The building model constructed on the DesignBuilder platform from different views.

223 224

The detailed design parameter information about the building envelope is described in Table 2.

225

The main structure adopts a frame-shear wall system without sunshades for windows. The building

226

was built in 1998. Thus, some design requirements of the building envelope cannot meet the Design

227

Standard for Energy Efficiency of Public Buildings GB 50189-2015 [51].

228 229

Table 2. Design parameter information of the building envelope. Component Exterior wall

Area (m2) 15,174

Roof

1650

Window Floors

5,058 -

Foundation

-

Materials 13 mm decorative brick + 20 mm lime mortar + 10 mm Expanded Polystyrene (EPS) insulation + 240 mm aerated concrete + 20 mm lime mortar 40 mm C20 fine aggregate concrete + 20 mm cement mortar + 5 mm waterproof membrane + 30 mm cement mortar + 60 mm Extruded Polystyrene (XPS) insulation + 20 mm cement mortar + 120 mm reinforced concrete + 20 mm cement mortar Aluminium frame + 6 mm clear single glazing 20 mm cement mortar + 100 mm reinforced concrete + 20 mm cement mortar 20 mm cement mortar + 80 mm fine aggregate concrete + 500 mm rammed clay

230 11

U-Value (W/(m2·K)) 0.5 0.35

6.073 2.813 0.887

Journal Pre-proof 231

A central air-conditioning system is utilised in this building, and the fan coil and ventilation

232

systems are also adopted in each space to control zones separately. In addition, there are operable

233

windows that allow natural ventilation in summer. Cooling systems continuously run from May to

234

September, and heating systems operate from December to February, because Wuhan is located in a

235

hot-summer and cold-winter zone. The HVAC system operation schedule is summarised in Table 3.

236

Different areas with specific functions in this building have different usage characteristics, including

237

the occupant density, illumination density, and equipment density, as displayed in Columns 5–7 of

238

Table 3.

239 240

Table 3. The HVAC operation schedule and indoor thermal disturbance settings. Function Business

Temperature Operation Setting Time

Cooling 26℃, Heating 20℃ Restaurant Cooling 26℃, Heating 20℃ Office Cooling 26℃, Heating 22℃ Conference Cooling 26℃, Heating 22℃

Illumination Power Density (W/m2) 15

Equipment Power Density (W/m2) 12

0.33

14

8

7:30~18:00

Mon - Fri 0.25

12

15

If use

1~2 days / 0.4 week

12

8

9:30~21:30

Operation Max Occupant frequency Density (p/ m2) Daily 0.3

10:00~21:00 Daily

241 242

4. Simulation Results and Validation

243

DesignBuilder is used to simulate the dynamic energy consumption of the selected high-rise

244

building over a one-year period on an hourly basis. Meanwhile, the actual energy consumption data is

245

collected using utility bills over a one-year period. Based on the monitored energy consumption, the

246

simulation model is verified for the following analysis. A comparison between the simulated and

247

monitored energy consumption data is conducted on a monthly basis, as displayed in Figure 3.

12

Journal Pre-proof Energy Consumption (MWh) 600 500

Heating_Simulated

400

Cooling_Simulated Others_Simulated

300

Equipment_Simulated

200

Lighting_Simulated Simulated Total

100

Monitored Total Moniored Total

0

248 249

Jan

Feb

Mar Apr May Jun

Jul

Aug Sep

Oct

Nov Dec

Figure 3. Comparison between the simulated and actual monthly energy consumption.

250

Overall, the actual annual energy consumption of this building is 3,712.63 MWh, and the energy

251

consumption per unit floor area reaches 107.15 kWh/m2. The simulation results show that the annual

252

energy consumption of the building is 3,559.52 MWh, with an energy consumption per unit floor area

253

of 102.73 kWh/m2. The comparison shows a relatively small difference, namely 4.1%, between the

254

simulated and the actual energy consumption data. The actual monthly energy consumption of this

255

building is often slightly higher than the simulated result, with a monthly difference of -3.1%, 4.5%, -

256

5.7%, -7.8%, -7.3%, -4.6%, -6.7%, -5.5%, -7.3%, 5.5%, -5.5%, and -4.2% from January to December,

257

respectively. Furthermore, according to Eqs. (3) and (4), the values of NMBE and CVRMSE are

258

calculated to be 4.5% and 6.2%, respectively, and these indices are within the tolerance range of the

259

Guideline Provisions in ASHRAE. Therefore, the model is reliable, and the simulation results are

260

acceptable.

261

Moreover, the consumed energy in cooling and heating systems changes significantly during

262

different seasons. The weather in Wuhan is relatively humid and cold from December to February,

263

indicating a high demand for long-term heating supply. The cooling system also greatly contributes to

264

energy consumption in the summer from June to September. The annual electricity consumed by the

265

air-conditioning system is estimated to be 1,742.78 MWh, accounting for 49% of the total power

266

consumption, where the cooling and heating systems consume 28% and 21% of the total energy,

267

separately. Thus, the building envelope cannot meet the current design requirements and has poor

268

insulation performance. There is a significant demand for retrofitting the building envelope in order to

269

achieve the required high efficiency for the building energy system.

270

5. Building Envelope Optimisation and Analysis 13

Journal Pre-proof 271

5.1. Level tests of orthogonal factors

272

In order to propose a feasible strategy of the envelope retrofit for energy savings, the optimisation

273

focuses on the thermal performance of the envelope. Five parameters of the architectural design are

274

addressed in this research, namely the exterior envelope fabric (A), exterior window type including

275

glass and window frames (B), sunshade type (C), WWR (D), and airtightness (E). These five

276

parameters are taken as the testing factors. Three levels are selected for each factor, and the test is

277

arranged by using an orthogonal table. The OAT experiments are described in Table 4, according to

278

the regulation [52]. The level classification of each factor is elaborated as follows.

279 280

Table 4. Orthogonal table of level tests for OAT experiments of the studied building envelope. Factor Description A B

C D E

Level 1

Exterior envelope 10 mm fabric (insulation thickness) Exterior window Plastic-steel window type frame + clear glass (3 mm) + air layer (13 mm) + clear glass (3 mm) Sunshade type Horizontal shading device (0.5 m) WWR 25% Airtightness The third grade

Level 2

Level 3

30 mm

50 mm

Plastic-steel window frame + coated clear glass (6 mm) + air layer (6 mm) + clear glass (6 mm) Horizontal shading device (0.8 m) 30% The fourth grade

Plastic-steel window frame + coated clear glass (6 mm) + air layer (13 mm) + clear glass (6 mm) Horizontal shading device (1.0 m) 35% The fifth grade

281 282

(1) Factor A: The building envelope fabric determines the comprehensive heat transfer coefficient

283

of the exterior wall. The building energy can be more efficiently saved when the coefficient is equal

284

to or less than 0.5 W/(m2·K). In the base model, the exterior wall structure is taken as the basic structure,

285

which includes a decorative brick with a thickness of 13 mm, a lime mortar with a thickness of 20 mm,

286

an EPS insulation layer with a thickness of 10 mm, and an aerated concrete layer with a thickness of

287

240 mm. The EPS insulation layers with a thickness of 10 mm, 30 mm, and 50 mm are selected as the

288

three levels of Factor A, and the corresponding comprehensive heat transfer coefficients for exterior

289

walls are 0.50 W/(m2·K), 0.404 W/(m2·K), and 0.336 W/(m2·K), respectively. The price of EPS is

290

estimated to be approximately 600 Chinese Yuan (CNY)/m3 [53].

291

(2) Factor B: The exterior window type includes the selection of the glass type and the window

292

frame type. Various glass and window frame materials result in different coefficient values of the heat

293

transfer and solar heat gain for exterior walls, although the window frame often accounts for a small

294

proportion of the exterior window area. In particular, the coated glass (Low-E glass) has a high 14

Journal Pre-proof 295

transmittance to near-infrared ray and high reflectivity to far-infrared ray. Moreover, the technical cost

296

is relatively low for improving window frames. The overall heat transfer performance mainly depends

297

on the heat transfer performance of the glass. The insulating glass or the glass combining insulating

298

glass with Low-E coatings are determined as presented in Table 4. The installation costs within three

299

levels are estimated to be approximately 1.39 million CNY, 1.74 million CNY, and 1.66 million CNY,

300

respectively [54, 55]. The comprehensive heat transfer coefficients of the three levels of exterior

301

windows reach 2.976 W/(m2·K), 2.722 W/(m2·K), and 2.283 W/(m2·K), respectively.

302

(3) Factor C: The window shading can directly block sunlight to the interior of a building,

303

reducing the heat transferred from sunlight into the indoor space and improving the thermal

304

environment inside the room in summer. The shading is divided into three types in terms of the relative

305

position of the shading device, namely exterior shading, internal shading, and double glazing medium

306

shading. The first type (exterior shading) is adopted as the heat absorbed by itself is carried away

307

through the air, and thus, there is no load in the room. In addition, its payback periods are usually less

308

than eight years for high-rise offices in southern China, according to the energy savings of the case

309

study in the work of Chen et al. [56]. The retrofit cost is roughly estimated at 67.3 thousand CNY for

310

a high-rise office with a height of 48 m and a WWR of 40%. In the present research, there are three

311

different levels of configuration for the window shading, including a horizontal exterior sunshade

312

board with a width of 0.5 m, 0.8 m, and 1.0 m, respectively.

313

(4) Factor D: The selection of WWR has been addressed for building envelope retrofit. The

314

change of window size can be implemented by (a) adding additional walls around the windows such

315

as in the case study building investigated by Zhou et al. [57] and Radwan et al. [58], or (b) by increasing

316

the window opening similar to the case building studied by Griego et al. [59]. The case building by

317

Zhou et al. [57] demonstrates that the annual total electricity consumption is decreased by 0.42%,

318

0.99%, 1.85%, 2.14%, 2.11%, and 1.94% when the south WWR of 0.2 is changed to 0.25, 0.3, 0.35,

319

0.4, 0.45, and 0.5, respectively, from which it can be observed that smaller WWR does not necessarily

320

mean lower energy consumption. This case building by Zhou et al. [57] has been successfully awarded

321

the Exemplar for Green Building in China as its operation is highly regarded after the retrofit. The case

322

building by Radwan et al. [58] demonstrates that decreasing the WWR of 0.4 to 0.3 reduces the annual

323

total electricity consumption by 4.35%. Moreover, in the research by Griego et al. [59], different types

324

of glazing were investigated for retrofit, and the single pane low-transmissive glazing at the WWR of

325

0.3 reaches the optimum fenestration configuration when adopting WWR as a retrofit factor for office

326

buildings. Taking the typical office floor as an example in the present research, the office area near the

327

windows and the corridor area reach 596 m2 and 120 m2, respectively. It is estimated that the area of

328

exterior windows should be larger than 131 m2. Further results are obtained that WWR should be larger 15

Journal Pre-proof 329

than 24%. The final three levels of WWR are 25%, 30%, and 35%, respectively. Corresponding

330

installation costs are estimated to be approximately 3.57 million CNY, 8.84 million CNY, and 10.31

331

million CNY [60].

332

(5) Factor E: The airtightness significantly affects building energy consumption. A higher

333

airtightness level indicates more energy savings and more expensive costs. In this research, the exterior

334

windows with an airtightness grade of 3, 4, and 5 are selected for the three levels of Factor E. The

335

corresponding air ventilation frequency at each level is 0.30 ac/h, 0.25 ac/h, and 0.20 ac/h, respectively.

336

The installation cost for different levels is estimated at approximately 2.08 million CNY, 2.34 million

337

CNY, and 2.58 million CNY, respectively [60, 61].

338

5.2. Orthogonal design and factor analysis

339

There are five factors (i.e., A, B, C, D, and E) in the orthogonal design and three levels (i.e., 1, 2,

340

and 3) for each factor, and thus, the orthogonal table L18 (37) is selected according to the test setup

341

principle in OAT. Later, 18 scenarios are generated and simulated to obtain the building energy

342

consumption data by using DesignBuilder. Table 5 illustrates the results of the orthogonal experiment

343

for the energy consumption of the building envelope. Each test is regarded as a DMU in the DEA

344

benchmarking. The results reveal that the lowest energy consumption is derived from Test No.15

345

(A2B3C1D2E3), where the corresponding annual energy consumption reaches 3,333.383 MWh,

346

associated with an annual energy savings of 226.137 MWh.

347 348

Table 5. Orthogonal experimental results for the energy consumption of the building envelope. Factor Level

Test No. (DMU) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

A: Envelope Fabric (x1: thickness, mm) 1 (10) 1 (10) 1 (10) 2 (30) 2 (30) 2 (30) 3 (50) 3 (50) 3 (50) 1 (10) 1 (10) 1 (10) 2 (30) 2 (30) 2 (30) 3 (50) 3 (50) 3 (50)

Energy Consumption, Energy MWh saving per B: Window Type C: Shading D: WWR E: Airtightness (y1: Energy Saving a, unit area Type (x2: cost, million (x4: cost, (x5: cost, MWh) kWh/m2/year CNY) (x3: width, m) million CNY) million CNY) 1 (1.39) 2 (1.74) 3 (1.66) 1 (1.39) 2 (1.74) 3 (1.66) 1 (1.39) 2 (1.74) 3 (1.66) 1 (1.39) 2 (1.74) 3 (1.66) 1 (1.39) 2 (1.74) 3 (1.66) 1 (1.39) 2 (1.74) 3 (1.66)

1 (0.5) 2 (0.8) 3 (1.0) 1 (0.5) 2 (0.8) 3 (1.0) 2 (0.8) 3 (1.0) 1 (0.5) 3 (1.0) 1 (0.5) 2 (0.8) 2 (0.8) 3 (1.0) 1 (0.5) 3 (1.0) 1 (0.5) 2 (0.8)

1 (3.57) 2 (8.84) 3 (10.31) 2 (8.84) 3 (10.31) 1 (3.57) 1 (3.57) 2 (8.84) 3 (10.31) 3 (10.31) 1 (3.57) 2 (8.84) 3 (10.31) 1 (3.57) 2 (8.84) 2 (8.84) 3 (10.31) 1 (3.57)

16

1 (2.08) 2 (2.34) 3 (2.58) 2 (2.34) 3 (2.58) 1 (2.08) 3 (2.58) 1 (2.08) 2 (2.34) 2 (2.34) 3 (2.58) 1 (2.08) 1 (2.08) 2 (2.34) 3 (2.58) 3 (2.58) 1 (2.08) 2 (2.34)

3,450.128 (109.392) 3,426.089 (133.431) 3,350.057 (209.463) 3,410.911 (148.609) 3,402.667 (156.853) 3,388.387 (171.133) 3,370.344 (189.176) 3,440.368 (119.152) 3,367.542 (191.978) 3,448.943 (110.577) 3,382.280 (177.240) 3,406.409 (153.111) 3,471.258 (88.262) 3,440.640 (118.880) 3,333.383 (226.137) 3,388.186 (171.334) 3,460.433 (99.087) 3,347.980 (211.540)

3.16 3.85 6.05 4.29 4.53 4.94 5.46 3.44 5.54 3.19 5.12 4.42 2.55 3.43 6.53 4.94 2.86 6.11

Journal Pre-proof 349 350

Note: a. Energy savings = Energy consumption - Baseline, where the baseline indicates an annual energy consumption of

351

According to the orthogonal design (see Table 5), six tests are conducted at each level of the five

352

factors. The mean value, 𝑇𝑞 (𝑞 = 1, 2, and 3), of test results at each level can be obtained. Figure 4

353

displays the influence rules of factor levels on the total energy consumption in the process of the

354

envelope optimisation. Taking Factor A as an example, the building energy consumption at levels 1,

355

2, and 3 turns out to be 3,410.651 MWh, 3,407.874 MWh, and 3,395.809 MWh, respectively. That is

356

to say, Factor A achieves the lowest building energy consumption at Level 3. In the same way, Factors

357

B, C, D, and E achieve the lowest building energy consumption at Level 3, Level 1, Level 1, and Level

358

3, respectively. When the optimal level of each factor is combined, a new DMU represented by

359

A3B3C1D1E3 is generated.

3,559.52 MWh.

360

Furthermore, Table 6 presents the results of the variance analysis. The analysis demonstrates that

361

the exterior window type and airtightness are highly significant factors affecting the building energy

362

consumption. The envelope fabric and WWR could be considered as significant factors owing to Sig.

363

< 0.005. However, the shading type could be freely selected after the strategy is optimised.

364

365 366 367 368

Figure 4. Analysis of the generated orthogonal tests among five factors in building energy consumption.

369

Table 6. Results of the variance analysis of the building energy consumption. Factor

Sum of

Degree of

Mean

Squares

Freedom

Deviation

F

F0.05

Sig.

A

747.152

2

373.576

10.154

4.74

Significant

B

13,809.172

2

6,904.586

187.676

4.74

Highly significant

17

Journal Pre-proof C

228.346

2

114.173

3.103

4.74

None

D

1,358.911

2

679.456

18.468

4.74

Significant

E

12,724.426

2

6,362.213

172.933

4.74

Highly significant

Deviation

257.53

7

36.79

370 371

6. Determination of Building Envelope Optimisation Strategy

372

As discussed in the previous section, the envelope fabric, exterior window type, WWR, and

373

airtightness have significant effects on building energy consumption. Thus, the lowest average energy

374

consumption should be addressed. The configuration is a 50-mm EPS insulation layer, plastic steel

375

window frame + 6-mm coated clear glass + 13-mm air layer + 6-mm clear glass, a WWR of 25%, and

376

airtightness of Grade 5 for the exterior window. The shading type is labeled as an insignificant factor.

377

As suggested in the range analysis, the horizontal shading with a width of 0.5 m can produce the

378

optimal shading effect. Thus, A3B3C1D1E3 should also be taken into account and seen as the 19th DUM,

379

namely 𝐷'=19, in Eqs. (13) and (14). Its annual total energy consumption reaches 3,316.06 MWh.

380

As listed in the parentheses of Table 5, four input variables are insulation thickness, sunshade

381

type, and two economic factors, and they are represented by 𝑥𝑖 in Eqs. (13) and (14). The energy

382

savings is selected as the output variable, 𝑦𝑗, and is listed in parentheses in the second-to-last column.

383

Therefore, 𝑥1, 𝑥2, 𝑥3, 𝑥4, 𝑥5, and 𝑦1 represent the thickness of the EPS insulation layer, the window-

384

type-related installation cost, the width of the horizontal exterior sunshade board, the WWR-related

385

installation cost, the window-airtightness-related installation cost, and the annual energy savings,

386

respectively. The DEA problem is then addressed based on the work of Ji and Lee [62]. Table 7

387

illustrates the efficiency scores of the selected retrofit solutions. It can be observed from the efficiency

388

scores presented in Table 7 that the most efficient strategies are the 3rd DMU, the 11th DMU, the 15th

389

DMU, and the 19th DMU.

390 391

Table 7. Efficiency scores of the selected retrofit solutions. DMU

Test

Efficiency

Rank

DMU

Test

Efficiency

Rank

1

A1B1C1D1E1

0.73

13

11

A1B2C1D1E3

1.00

1

2

A1B2C2D2E2

0.76

12

12

A1B3C2D2E1

≈1.00

7

3

A1B3C3D3E3

1.00

1

13

A2B1C2D3E1

0.48

19

4

A2B1C1D2E2

0.79

11

14

A2B2C3D1E2

0.62

17

18

Journal Pre-proof 5

A2B2C2D3E3

0.69

14

15

A2B3C1D2E3

1.00

1

6

A2B3C3D1E1

≈1.00

5

16

A3B1C3D2E3

0.91

9

7

A3B1C2D1E3

≈1.00

8

17

A3B2C1D3E1

0.50

18

8

A3B2C3D2E1

0.63

15

18

A3B3C2D1E2

≈1.00

6

9

A3B3C1D3E2

0.87

10

19

A3B3C1D1E3

1.00

1

10

A1B1C3D3E2

0.63

16

392 393

The annual total energy consumption of the 19th DMU reaches 3,316.06 MWh, which is lower

394

than other scenarios in the orthogonal table (see Table 5). Thus, the 19th DMU is the most efficient and

395

energy-saving retrofit strategy. The proposed optimisation strategy is A3B3C1D1E3. Compared with the

396

base model with an annual total energy consumption of 3,559.52 MWh, the current strategy could

397

decrease the energy consumption by approximately 243 MWh and increase the annual energy savings

398

to 7.01 per unit area (kWh/m2). The results also reveal that optimising the envelope fabric of

399

commercial high-rise buildings could cause greater energy efficiency in hot summer and cold winter

400

areas.

401

7. Conclusions

402

As the demand for powering buildings is increasing rapidly in urban areas, optimising the building

403

envelope is considered an effective solution that can help both commercial and individual investors

404

offset their daily power usage and reduce their overall costs. There is, however, an urgent need to seek

405

a highly-efficient approach to manage numerous design scenarios in order to achieve energy

406

optimisation objectives. Thus, the research presented in this paper proposed a novel hybrid approach

407

that integrates the building energy simulation technologies, OAT, and DEA to identify optimal

408

solutions for building retrofit. The integrated method is capable of reducing the number of scenarios

409

through OAT and seeking the most efficient solution through DEA based on energy simulation for

410

building retrofit. A commercial high-rise building in Wuhan is selected as a case study. Its energy

411

consumption is simulated using the DesignBuilder platform, and the reliability of the model is verified

412

using utility bills. The validated model serves as a baseline for the subsequent retrofit scenarios. Based

413

on simulation and optimization, retrofit solutions are then proposed for each design parameter with

414

high energy-saving potentials. A limited number of tests are required to seek a new range of optimal

415

retrofit solutions through the OAT strategy. The DEA BCC model is then adopted to identify the most

416

efficient solutions. Finally, an optimal envelope is determined to improve the energy performance of

417

high-rise buildings. 19

Journal Pre-proof 418

The results reveal that: (1) hundreds of tests have been significantly reduced to 18 scenarios

419

through the OAT approach. (2) The variance analysis demonstrates that exterior window type and

420

airtightness are highly significant factors affecting the building energy consumption. (3) In terms of

421

building energy consumption, the optimal configuration turns out to be a 50-mm EPS insulation layer,

422

plastic steel window frame, 6-mm coated clear glass + 13-mm air layer + 6-mm clear glass, the WWR

423

of 25%, and airtightness of Grade 5 for the exterior window. The horizontal shading with a 0.5-m

424

width can produce the optimal shading effect. (4) The retrofit solution can save 7.01 kWh/m2 of

425

operation energy per year, which is also identified to be cost-effective by DEA. Overall, the

426

optimisation approach can be beneficial for decision-makers to minimise the energy consumption of

427

high-rise buildings in the envelope design and retrofit projects. The research can contribute to judicious

428

building retrofit and the efficient use of building materials.

429

In this research, the simulated energy consumption from a well-verified simulation platform is

430

used to perform multi-factor analysis and optimization through benchmarking, based on which more

431

design factors can be considered to conduct more comprehensive retrofit plans in future research.

432

Moreover, the correlation between these factors (e.g., artificial lighting may be greatly affected by the

433

WWR) will also be evaluated in future research. In addition, the energy consumption data are uncertain

434

due to estimation and measurement errors in real life; therefore, uncertainty and sensitivity analysis

435

will also be conducted to discover optimal solutions under uncertainty for building retrofit.

436

Acknowledgments

437

The National Natural Science Foundation of China (Grant No.51708282), the Start-Up Grant at

438

Nanyang Technological University, Singapore (No. M4082160.030), and the Ministry of Education

439

Grant, Singapore (No. M4011971.030) are acknowledged for their financial support of this research.

440

Conflict of Interests

441

The authors declare that they have no competing interests.

442

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443

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Journal Pre-proof Conflict of interest statement

We declare that the manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript

Authors: Hong Xian Li, Boya Jiang, Yan Li, Limao Zhang, Xianguo Wu, Jingyi Lin

Journal Pre-proof Highlights  A novel hybrid approach that integrates computer simulation, OAT, and DEA is developed.  It is able to discover the optimal strategy for energy saving in building retrofits.  A high-rise building is used to demonstrate the applicability and effectiveness of the approach.  The optimal solution is capable of saving the annual operation energy of 7.01 kWh/m2.  Window type and airtightness are significantly important factors in energy performance saving.