Evaluation of cost-effective building retrofit strategies through soft-linking a metamodel-based Bayesian method and a life cycle cost assessment method

Applied Energy 253 (2019) 113573

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Applied Energy journal homepage: www.elsevier.com/locate/apenergy

Evaluation of cost-effective building retrofit strategies through soft-linking a metamodel-based Bayesian method and a life cycle cost assessment method

T

Jun Yuana, Victor Nianb, , Bin Sub ⁎

a b

China Institute of FTZ Supply Chain, Shanghai Maritime University, China Energy Studies Institute, National University of Singapore, Singapore

HIGHLIGHTS

integrated metamodel and cost-effectiveness analysis method is proposed. • An method is computationally efficient and transparent in cost assessment. • This • This method can help identify trade-offs among building retrofit options. ARTICLE INFO

ABSTRACT

Keywords: Building energy retrofit Cost effectiveness Energy efficiency Metamodel Bayesian model

The building sector contributes a major proportion of the global energy consumptions and carbon emissions. The energy performance or efficiency of buildings can be improved through a wide range of retrofitting measures which can have very different costs. Under budget, time and other resource constraints, it is not practical to apply all energy saving measures to a given retrofitting project. As such, there is a need to rank and select the most cost-effective measures to meet efficiency improvement goals. Traditionally, energy efficiency improvement measures and their costs are evaluated separately which makes prioritising among the measures difficult. In response, an integrated approach by soft-linking a metamodel-based Bayesian method and a life cycle cost assessment method is proposed to rank and select the most cost-effective retrofitting measures. The metamodelbased method is used to compute building energy consumptions before and after retrofit; and the cost-assessment method is used to evaluate the life cycle cost of implementing each measure. A selection of nine retrofitting measures are ranked according to life cycle energy savings, life cycle cost, and cost-effectiveness (measured by cost per unit energy saved). Findings from the Singapore case study suggest that retrofitting building envelop is the third least cost-effective measure although it can lead to highest energy savings. Lighting replacement has the least life cycle energy savings, but it is the most cost-effective measure. Electricity price has little influence on the cost-effectiveness ranking of all nine measures but discount rates (tested for 4%, 7% and 12%) can influence the ranking of home appliances. Based on the findings from the case study, the proposed integrated approach can help identify an optimum retrofit strategy and the cost of achieving energy efficiency targets for existing buildings.

1. Introduction The building sector is accountable for more than 40% of global energy consumption [1] which translates to more than 19% of global energy-related carbon emissions [2]. Projections by the Intergovernmental Panel on Climate Change (IPCC) show that building energy use and hence carbon emissions may double or even triple by 2050 [3]. Since existing buildings constitute a large proportion of the total building stock, improving the energy performance of existing buildings

⁎

is crucial to reduce the building sector energy consumption and carbon emissions [4]. Retrofit demonstrates an important strategy in improving the energy performance of existing buildings among others such as building systems optimization and operational improvements. It has been shown that building retrofits could achieve 50–90% energy savings in existing buildings worldwide [3]. Deriving energy savings through retrofit and operational measures is usually constrained by cost among other considerations. Very often, innovative technologies that are highly efficient usually have a

Corresponding author. E-mail address: [email protected] (V. Nian).

https://doi.org/10.1016/j.apenergy.2019.113573 Received 15 October 2018; Received in revised form 3 June 2019; Accepted 17 July 2019 0306-2619/ © 2019 Elsevier Ltd. All rights reserved.

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premium price tag. It is impractical to implement all of the best-in-class technologies and/or measures when cost is an important decision factor. Therefore, it is necessary to simultaneously consider both cost and effectiveness in reducing energy consumption when evaluating the suitability of retrofit measures. The ranking and selection of retrofit measures for existing buildings has been a popular research topic in recent years [5–7]. The ranking of retrofit measures is usually accomplished through achieving multi-objectives [8,9] and/or with the help of indicators. Minimizing the cost of retrofit, maximizing carbon emission reductions, and optimizing social and/or environmental objectives are examples of the multiple objectives needed to be achieved. However, it is usually not easy to solve a multi-objective problem because expert opinions are often required to consider the trade-off among the different objectives. The results arising from such expert options could make the ranking results subjective or even biased. Another more commonly used approach is to use indicators which incorporates the trade-off among different objectives. Common indicators include the payback period [10] and annualized costs [11]. A number of methods for evaluating the objectives and/or indicators can be found from the literature. Ibn-Mohammed et al. [12] proposed an approach to evaluate and identify economically efficient building retrofit measures which can achieve the largest emissions reductions. Ashrafian et al. [13] developed a framework to identify building retrofit measures considering both cost and energy savings. Chidiac et al. [11] applied a regression approach to assess the effects of different building retrofit measures. McArthur and Jofeh [14] proposed an approach to identify strategic investments for building retrofit measures in a building portfolio. Jafari and Valentin [15] developed an approach to identify optimal retrofit measures for a residential building based on energy consumption savings. These methods are usually dependent on the real observed or measured data from existing buildings which may be challenging to obtain. As such, simulation models are often developed to represent the physical and operational characteristics of existing buildings. In addition, simulation-based optimization methods have also been developed to identify optimal retrofit strategies to achieve energy efficiency targets [5]. Asadi et al. [16] used a TRNSYS-GenOpt and MATLAB based multiobjective optimization model to rank and select retrofit measures. Asadi et al. [17] proposed a genetic algorithm and artificial neural network based model to identify energy retrofits. Wang et al. [18] developed an optimization model to assess retrofit measures by maximizing energy savings and operational cost savings. Ferrara et al. [5] proposed a cost optimal configuration of near net zero energy buildings. Malatji et al. [19] developed a multi-objective optimization model for building retrofit measures that optimizes energy savings and payback period. Shao et al. [20] applied a multi-objective optimization model and stakeholder requirement analysis based framework to identify retrofit measures. Zhivov et al. [21] developed an energy optimization method for improving operational efficiency of army buildings. The ranking and selection of retrofit measures are also affected by different design aspects and climate conditions. For instance, the design with green roofs can affect the building energy savings and the performance of retrofit measures [22]. Huang et al. [23] studied the effects of different climates on the performance of retrofit measures, which indicates that different climates may result in different optimal retrofitting schemes. Sun et al. [24] investigated the cost-effectiveness of design strategies for building retrofit in tropical climate. Therefore, it is important to consider the design and climate effects on retrofit measures evaluation. The advantage of using simulation model is that it can account for the design and climate conditions in the model. Hence, the ranking and selection results can be obtained considering different design aspects and climate conditions. Although the simulation-based methods have been widely used, the use of energy simulation models tend to restricted to only trained professionals [11]. Moreover, simulation models are usually time

consuming even for a single model run due to the size and complexity of building systems [25]. In most applications, a large number of simulation runs may be required before credible conclusions can be drawn which makes the use of simulation-based modeling analysis rather inefficient. In the attempt to address computational inefficiency, metamodels are often used as simpler and faster approximations of the large and complex simulation models [26]. Among various metamodels, Gaussian process (GP) is one of the most popular metamodel due to its flexibility [27]. GP has also been applied to evaluate building retrofit measures such as model calibration [28]. Yuan et al. [29] proposed a GP-based method to calibrate building energy models and simultaneously rank the retrofit measures according to their effectiveness in reducing building energy consumptions. However, the authors considered energy savings as the only indicator in their ranking. It is also important to incorporate cost as an important objective into the evaluation method. To consider both cost and energy savings, the cost-benefit analysis method is commonly used when evaluating retrofit measures. This method seeks to translate both cost and benefit of retrofit measures into monetary terms. However, it is not always possible to directly translate all outcomes into monetary terms [30]. Another commonly used method is the cost-effectiveness analysis (CEA) method [24]. According to findings from [31], CEA tends to be more efficient than other methods such as cost benefit analysis and multi-criteria analysis. CEA can be used to select suitable retrofit measures by balancing the life cycle cost and energy savings [32]. As such, CEA based on life cycle cost assessment is well suited for the economic appraisal of energy efficiency improvement measures for buildings [33]. In summary, both the metamodel based methods and cost-effectiveness analysis methods have been independently applied to evaluate building retrofit measures. The use of metamodel can avoid the frequent use of complex simulation model. The cost-effectiveness analysis can consider both cost and energy saving objectives. However, we have not found any study on the assessment of cost-effectiveness for building retrofit measures using a GP model. In response, a systematic framework of soft-linking CEA and metamodel is proposed to rank and select building retrofit measures taking into consideration cost and energy savings. The proposed framework incorporates the advantages of both metamodeling techniques and cost-effectiveness analysis, which is efficient and accurate for multi-objective ranking and selection. The CEA component is used to rank and select the retrofit measures by assessing their cost effectiveness, where both cost and energy saving objectives are considered. The Bayesian metamodel component is used to represent the building energy model which is useful for predicting energy consumptions under the influence of different retrofit measures. The using of metamodel is much more efficient than directly using the physical building or simulation model, and the using of Bayesian framework can account for various uncertainties in retrofit measures evaluation. The output of the framework is an indicator used to evaluate the cost effectiveness of each retrofit measure. Finally, the retrofit measures are ranked and selected to meet both energy efficiency targets and cost considerations. The paper is organized as follows. Section 2 provides an integrated procedure to rank and select retrofit measures combing the metamodel based method and the CEA method. Section 3 presents the metamodel development and further proposes a method to calibrate the model and predict the building energy consumption for different scenarios. Section 4 describes the proposed indicator to assess the cost effectiveness of retrofit measures. Section 5 presents a case study to illustrate the benefits of the proposed method. Section 6 concludes the paper with recommendations for future research. 2. An integrated procedure to rank and select retrofit measures The overall procedure to rank and select cost effective retrofit measures is visualized as shown in Fig. 1 and as explained in the next 2

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Fig. 1. An integrated procedure to rank and select cost effective retrofit measures.

paragraph. The first step is to identify applicable retrofit measures by examining the physical conditions of existing building in conjunction with expert opinions. After identifying all the possible retrofit measures, the cost and energy saving potentials of each measure are evaluated. The cost of each retrofit measure is evaluated based on the investment cost and operation and maintenance (O&M) cost [34]. The energy savings potential of each retrofit measure can be evaluated through a metamodel based method, such as GP-based method. The GP model is used to approximate a building energy systems model which is usually represented by engineering simulation tools, such as EnergyPlus or DOE2. After calibration, the calibrated model is used to predict the energy savings potential of each retrofit measure. With energy consumption and cost assessment, the cost-effectiveness of all the retrofit measures can be further analyzed. Based on cost-effectiveness analysis, retrofit measures can be ranked and selected for policy making. In the next section, the metamodel based method is discussed in detail.

usually unknown in the existing building but their values have to be specified in the simulation model. Therefore, the model has to be calibrated before intended use. As simulation model is an approximation to the existing building, the discrepancy between the existing building and the simulation model often exists which is denoted by δ(x). Then the relationship between the observed energy consumption from existing building and the simulated energy consumption from simulation model can be represented as follow equation.

z (x ) = y (x , ) + (x ) + e

(1)

where e represents the observation error in energy consumption measurement from existing building. Based on model form given by Eq. (1), Gaussian process model is further developed. It is assumed that the simulation output y(x,θ) and the discrepancy δ(x) are both Gaussian process. Specifically, y(x,θ) is assumed to be a Gaussian process with unknown mean function μy and covariance function y2 Ry , and δ(x) is assumed to be a Gaussian process with unknown mean function μδ and covariance function 2 R . For the Gaussian process, unknown mean function is often assumed to be constant which is reasonable in many applications [35]. y2 and 2 are the unknown variance. Ry and R are the correlation functions where different correlation forms can be used [36]. Here the commonly used Gaussian correlation is adopted which has the following form between any two input settings x and x’.

3. Energy consumption assessment 3.1. Model development The simulation model is commonly developed to represent the existing building. With both physical building and simulation model, the available data include the real observations from existing building and the simulated outputs from simulation model. Here the output of interest is the energy consumption of the building. Let z(x) denote the observed energy consumption from existing building and y(x,θ) denote the simulated energy consumption from simulation model, where x denote the input variables considered in the model such as external temperature and θ denote the unknown optimal values of the calibration parameters such as infiltration rate. Calibration parameters are

R (x , x ') = exp(

x

x' )

(2)

where is the decaying parameter. There is also an observation error e in Eq. (1), which is assumed to be a zero mean normal distribution with unknown variance e2 . Given the model form and assumptions, there are various unknown parameters in the developed GP model, including the calibration parameter θ, the Gaussian process mean μ, the variance σ2, and the 3

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decaying parameter ϕ. These unknown parameters have to be estimated before applying the model for further analysis.

3.3. Energy consumption prediction With the estimated parameters, the GP model can be used to predict the building energy consumptions before and after retrofit. Any unobserved input set indicates a scenario where the energy consumption is unknown. The interest is to predict the energy consumption for these unobserved scenarios. Here the predictive distribution is derived. At any input x0, given the observed data d and all the parameters ξ= {μ,σ2,ϕ} and θ, it can be derived that the energy consumption prediction z (x 0) has a normal distribution with mean µz (x 0 )|d, , and variance 2 z (x 0 )|d, , given as follows.

3.2. Model calibration In this study, we propose to use the Bayesian method to estimate the unknown parameters including the calibration parameters based on the proposed GP model. The Bayesian method has been widely used for parameter estimation. Let D denote the observed data and denote the unknown parameters. Based on the Bayes’ theorem, the posterior distribution of can be derived as following equation.

f ( |D)

µz (x 0 )|d,

(3)

f ( ) f (D| )

2,

, |d )

f (µ ,

2,

, ) f (d|µ ,

2,

, )

2 z (x 0 )|d, ,

A

B

f (µ ,

2,

, |d ) dµd 2d

= Var (z (x 0 )|d, , ) =

2 y

2

+

V0 (x 0 , )T Vd ( ) 1V0 H T Vd ( ) 1V0 (x 0 , ))T (H T Vd ( ) 1H )

(x 0 , ) + (1 (1 where

H=

2 y Ry (Dy , 2 y Ry (Dz

1n 0m 1n 1m

T

1

(7)

H T Vd ( ) 1V0 (x 0 , ))

V0 (x 0 , ) = (

Vd ( ) =

2 y Ry ((x 0 ,

), Dy ),

), Dz ( )) +

2 y Ry (Dy ,

Dy )

( ), Dy )

2 y Ry ((x 0 ,

2 y Ry (Dz

( ), Dz ( )) +

2

R (x 0 , Dz ))T

Dz ( )) 2

R (Dz , Dz ) +

2 e Im

andµd = H (µ y , µ )T

Here (x 0 , ) denote the input set at x 0 with . Dy denote the input sets where computer model outputs are available. Dz ( ) and Dz denote the input sets where real observations are available with and without respectively. Ry (·,·) and R (·,·) denote the correlations among different input sets with the Gaussian correlation form. n and m denote the number of computer model outputs and real observations respectively. Im is the m × m identity matrix. 1n denote n dimension vector of 1, 0m and 1m denote m dimension vector of 0 and 1 respectively. It can be found that the derived predictive distribution depends on the parameters θ and ξ. These parameters can be further integrated out using numerical integration methods (e.g. MCMC) so as to account for their uncertainties. For example, the calibration parameter can be integrated out in the mean and variance as follows.

µz (x 0 )|d, =

(4)

where f(μ,σ2,ϕ,θ) denote the prior density function for all the unknown parameters and f(d|μ,σ2,ϕ,θ) denote the likelihood function. To estimate all these parameters, it is of interest to obtain the marginal posterior of each parameter. For instance, the marginal posterior of calibration parameter, f(θ|d), can be used for calibration purpose. The marginal posterior can be derived using Eq. (5).

f ( |d ) =

µd ) (6)

where the posterior of (i.e. f ( |D) ) is proportional to the prior of (i.e. f ( ) ) times the likelihood of (i.e. f (D| ) ). The posterior of which is conditional on the observed data D can be used to make inference about the unknown parameters. The first step to using the Bayesian method is to assign priors for these unknown parameters. Based on practical applications, different priors can be assigned, such as non-informative prior and conjugate prior. If there is no or little information about the parameters, the noninformative prior is usually used. Non-informative priors are vague, diffuse and flat priors such as uniform prior, which usually have little impact on the posterior distributions. For mathematical convenience, the conjugate prior is often adopted, where the prior and posterior distribution are from the same family. The choice of priors can be found in [35]. Here Gaussian process mean μ is assumed to be a normal distribution conditional on variance σ2. Variance σ2 is assumed to be an inverse Gamma distribution. Decaying parameter ϕ is assumed to be a Gamma distribution. The prior for the calibration parameter θ depends on the case itself. For most cases, little information can be obtained for the calibration parameters, where the non-informative priors such as uniform priors are often used. Next, the likelihood function is further derived. The total set of data available includes the real observations from existing building and the simulated outputs from simulation model. Let d denote the total data set. Then d is assumed to follow a normal distribution given all the parameters. Based on this, the likelihood function can be derived. The details are provided in [29]. Given the prior and likelihood, the posterior of the all the parameters, f(μ,σ2,ϕ,θ|d), can be derived using Eq. (4).

f (µ ,

= E [z (x 0 )|d, , ] = (µ y + µ ) + V0 (x 0 , )T Vd ( ) 1 (d

,

2 z (x 0 )|d,

=

µz (x 0 )|d, 2 z (x 0 )|d, ,

,

f ( |d, ) d

(8)

f ( |d, ) d

(9)

With the derived predictive distribution, the energy consumption for any input set can be predicted. Therefore, the energy consumption for different retrofit scenarios can be assessed. Here the metamodel is used to predict the energy consumption other than using the simulation model. This is much more efficient as it requires much less simulation runs. If the simulation run is time consuming, the metamodel based method is preferred. Other than the assessment of the energy consumption, it is also required to assess the cost of each retrofit measure and further assess its cost effectiveness. A proper indicator for cost effectiveness analysis is provided in next section.

(5)

where A, B and Φ denote the domains of μ, σ2 and ϕ respectively. However, it is usually not possible to integrate out the other parameters to obtain the analytical posterior distribution. Therefore, numerical methods are often used, such as the Markov chain Monte Carlo (MCMC) method [37]. Numerical methods have been widely used in practice due to their flexibility. Here the Gibbs sampling method is used as conditional distributions of all the unknown parameters can be derived analytically. Then the samples of all the parameters can be obtained by iteratively sampling from the derived conditional distributions. These samples can be used to make inference about the unknown parameters. For instance, the unknown parameters can be estimated as their posterior mean or posterior mode.

4. Cost effectiveness analysis We propose the use of three common indicators as explained in [32] and [31] to the conduct cost-effectiveness analysis. These three indicators are unit investment cost (UIC), unit annual cost (UAC) and dynamic generation cost (DGC). UIC can be computed by the total investment cost I over the energy saved E1 in the first year of operation. 4

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Table 1 Retrofit measures and corresponding parameters. Retrofit measure

Input parameter

Roof retrofit Wall retrofit Window replacement Major appliances replacement Lighting replacement Water heater replacement Air-conditioner replacement

UIC =

I E1

2

Roof U value (W/m K) Wall U value (W/m2 K) Window U value (W/m2 K) Clothes dryer power density (W/m2) Clothes washer power density (W/m2) Refrigerator power density (W/m2) Lighting power density (W/m2) Water heater power density (W/m2) EER

Ia + Ca Ea

1

d (1 + d )

A 31 years old 25-storey residential building located in Singapore with a Gross Floor Area (GFA) of 26,783 m2 is selected for this case study. There are total 175 units. The average number of occupants over 2017 is 542. The main appliances that contribute to the energy consumption include clothes washer, closes dryer and refrigerator. Water heater is also considered. The air-conditioner and the light used are assumed to be the same for all units as provided by the developer. The characteristics of the envelope, air-conditioner and appliances are given in Table 1. To simulate the building energy performance, an EnergyPlus model is developed. The retrofit measures considered in this study and their corresponding input parameters in the energy model are given in Table 1. The initial values for these input parameters before retrofit are also given in Table 1. The initial or design U values for roof, wall and window are based on information presented in [38]. The initial power densities for major appliances, light and water heater are estimated from real observed data. The initial energy efficiency ratio (EER) is obtained from the product description of the air conditioner. The selection of retrofit measures for this case study is based on considerations over the measures’ relevance in the tropical climate. There are also other retrofit measures such as floor retrofit and door replacement which are not considered here since they are much less relevant compared to the selected measures. Here we only focus on the selected retrofit measures. It should be noted that the performances of these retrofit measures are also influenced by the climate and building conditions. In order to consider the effects of climate and building conditions, their corresponding parameters can be treated as input variables in the developed model. In this paper, the climate and building conditions are treated as known for simplification. To consider the effects of climate and building conditions for more reliable results, various replications are conducted in the simulation model to generate different cases with different climate and building conditions. Then the average performances of these retrofit measures are evaluated so as to provide more reliable ranking results. When measuring the building’s energy performance, only electricity consumption is used as the only possible fuel consuming household devices are cook stove and possibly water heater. Furthermore, residential buildings in Singapore do not require space heating due to the tropical climate and electricity accounts for more than 90% of the residential building’s total energy consumed. Therefore, the objective of this study is to rank and select these retrofit measures by assessing their cost effectiveness in saving energy.

(12)

where I denotes the total investment cost, d denotes the discount rate, and N denotes the lifetime of the retrofit measure. UAC is better to estimate the long-term average cost effectiveness compared to UIC, as it takes into account the life time of the retrofit measures. However, one major drawback of UAC is that it only considers the time value of the costs, while time value of the energy savings is not accounted for. To handle this drawback, the DGC indicator is further proposed, where the energy savings are also discounted. This indicator is represented as the ratio of the discounted costs and the discounted energy savings, which is expressed by the follow equation.

DGC =

N Ik + Ck k = 0 (1 + d)k N Ek k = 0 (1 + d)k

0.13 0.12 1.00 3.7 4.8 8.6 10.8 8.65 10.8

5.1. Building descriptions

(11)

N

0.14 0.18 1.10 4 5 9 11 9 9.0

5. Case study

where Ia denotes the annualized investment cost, Ca denotes the average annual O&M cost, and Ea denotes the average annual energy saving. Ia can be expressed as

Ia = I

Expected value after retrofit

effectiveness of retrofit measures.

(10)

Although this indicator is simple and easy to calculate, it has several drawbacks. First, this indicator only considers the investment cost. The O&M costs are not taken into account. The O&M costs may also be an important part to assess the cost effectiveness of different retrofit measures. Second, the lifetime of the retrofit measure is not accounted for in assessing the cost effectiveness. Some retrofit measures with higher investment cost and longer lifetime may be preferred if the life cycle cost of the retrofit measure turns out to be competitive. Therefore, it is important to consider the life cycle cost when retrofit buildings. However, UIC is unable to reflect the effects of the lifetime of the retrofit measure. Third, only energy saved in the first year is used to assess the cost effectiveness. However, the energy saved may be different in every year. This indicator cannot account for these changes for energy savings. Therefore, it has been recommended in [31] that UIC may not be appropriate for professional cost effectiveness analyses although it provides an intuitive assessment on the upfront cost commitment to a given retrofit measure. Compared to UIC, UAC calculates the annual cost, which accounts for the O&M costs in addition to the investment costs. Moreover, UAC uses the average energy saving instead of the first-year energy saving in UIC. The UAC indicator can be expressed as

UAC =

Initial value

(13)

where Ik is the investment cost, Ck is the O&M cost, and Ek is the energy saving at year k. N is the lifetime of the retrofit measure and d is the discount rate. Therefore, in this indicator, both cost and energy saving in every year are discounted to the current year. It has been suggested in [31] that DGC is one of the ideal indicators to assess the cost effectiveness. In this paper, DGC is adopted as the indicator to assess the cost

5.2. Energy consumption and cost assessment This section provides the data description of the retrofit measures including their energy consumption and cost assessments. The retrofit 5

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Table 2 Cost components for different retrofit measures. Measures

Annual energy saving (kwh)

Investment cost (2012 US$)

O&M cost (2012 US$)

Life time (year)

Roof retrofit Wall retrofit Window replacement Clothes dryer replacement Clothes washer replacement Refrigerator replacement Lighting replacement Water heater replacement Air-conditioner replacement

14,077 168,926 140,771 70,386 46,924 84,463 23,462 82,117 93,682

506,564 1,681,202 1,907,246 183,600 175,950 229,500 13,745 510,000 198,900

−18,811 −225,739 −188,115 −61,409 −42,997 −88,383 −2158 −71,644 −70,301

30 30 30 13 14 17.4 5 13 10.5

measures are evaluated one-at-a-time assuming only one measure is applied each time. In practise, multiple options may be available under each measure, such as different chiller system of different EER values and different window systems of different U values. The energy saving potential for retrofit measure is assessed based on the values of the input parameters before and after retrofit. The values before retrofit (initial values) and the expected values of these parameters after retrofit are given in Table 1, which are adopted from [39]. For each retrofit, the proposed metamodel is used to predict the energy consumption as described in Section 3 for input parameter sets before and after retrofit. The energy savings are then calculated based on the difference in the predicted energy consumptions before and after retrofit. As the predicted energy consumptions are also influenced by the climate conditions, 1000 cases with different climate conditions are generated in the simulation model and the energy consumption is predicted for each case. Then the average energy consumption over 1000 cases is used to hedge the effects of the climate conditions. The predicted annual energy saving for each measure is given in Table 2. The two sample t-test is further used to examine the difference in energy saving between any two retrofit measures. The results indicate that the energy savings for different retrofit measures are all significant different from each other. The investment costs of retrofit measures are adopted from [39]. The annual O&M cost except the electricity cost is assumed to be the same before and after the retrofit. However, the cost of electricity is reduced after retrofit due to the savings in electricity consumption after applying the retrofit measures. In this study, the reduced electricity cost is treated as the savings in O&M cost. As such, the values of O&M costs are given a negative sign to indicate reductions in O&M costs as a result of energy savings. The annual cost savings due to reduced electricity consumption is calculated based on the assumed electricity price taken from [40] and annual electricity savings modelled using the proposed metamodel method. The average electricity savings are obtained over 1000 cases with different climate conditions. The reason for using EIA’s electricity price projection is twofold. There is no available information on the projected electricity price in Singapore and the focus of this paper is not into the projection of future electricity prices. The life cycle cost

reductions due to savings in electricity consumption is computed by adding up all discounted annual cost saving over lifetime of each measure. The discounted rate is assumed to be 7% which is also adopted from [40]. The lifetime for each measure is obtained from [39]. The cost components for each measure and the measure’s lifetime are given in Table 2. 5.3. Numerical results 5.3.1. Retrofit measures ranking and selection results The cost-effectiveness of retrofit measures represented as a DGC value is given in Table 3 based on energy savings and cost assessment. The two sample t-test is further used to examine the significance of the difference between any two retrofit measures. The test results indicate that the difference of cost-effectiveness between any two retrofit measures is significant. Therefore, the ranking results based on the costeffectiveness values are reliable. The retrofit measures are ranked according to their DGC value listed in the order from the smallest to the largest. It can be seen from Table 3 that lighting replacement is the best retrofit measure based on DGC value, followed by the air-conditioner replacement. These findings are consistent with those from reputable global studies that lighting and air-conditioner replacement are cost effective in improving residential building energy efficiency [41]. With reference to an earlier study [29], the case study results obtained in this paper differ significantly from those of the earlier study. Although the wall retrofit and the window retrofit have the largest and the second largest lifetime energy saving respectively, they are ranked as less cost-effective measures due to their higher life cycle cost. Therefore, the wall retrofit and the window retrofit are not the best choices when cost is taken into account. The life time energy saving amount for lighting replacement is ranked the second last with less than 100,000kwh. However, its lifetime cost is the lowest, which results in the smallest cost-effectiveness value. Therefore, the lighting replacement becomes the best choice. Based on the cost-effectiveness, lighting replacement, air-conditioner replacement, refrigerator replacement, clothes dryer replacement, clothes washer replacement and water heater replacement are ranked in high order, while the wall retrofit, window replacement and roof retrofit are not cost-effective.

Table 3 Ranking results from the proposed method. Measures

Lifetime cost (US$)

Rank by cost

Lifetime energy saving (kwh)

Rank by energy saving

Cost effectiveness (US $/kwh)

Rank by cost effectiveness

Lighting replacement Air-conditioner replacement Refrigerator replacement Clothes dryer replacement Clothes washer replacement Water heater replacement Wall retrofit Window replacement Roof retrofit

3906 128,599 141,117 122,191 132,953 438,356 1,455,463 1,719,131 487,753

1 3 5 2 4 6 8 9 7

83,640 498,486 464,556 379,700 254,771 442,983 665,740 554,780 55,478

8 3 4 6 7 5 1 2 9

0.047 0.258 0.304 0.322 0.522 0.990 2.186 3.099 8.792

1 2 3 4 5 6 7 8 9

6

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Fig. 2. Electricity price projections [40].

5.3.2. Sensitivity analysis The sensitivity analysis for electricity price and discount rate are further conducted to assess their effects on the ranking results. The high and low electricity price projections are shown in Fig. 2. The tested low, reference and high discount rates are 4%, 7% and 12% respectively. The cost effectiveness of retrofit measures under the high and low electricity price projections are computed as shown in Table 4. It can be seen that the ranking results are the same with the different electricity price scenarios although the values used for assessing cost-effectiveness differ. The cost effectiveness of retrofit measures at selected discount rates is computed as shown in Table 5. The discount rate has a stronger influence on the ranking results than electricity prices do as shown in Table 5. However, the influence of discount rate is mainly shown in altering the ranking sequence of appliance replacement as highlighted in bold font in Table 5. The ranking orders of lighting, building envelope, and roof retrofit measures remain unchanged.

effectiveness, followed by the air-conditioner replacement and the refrigerator replacement. Roof retrofit performs worst in terms of costeffectiveness. Wall replacement and window replacement have the largest energy saving. However, they are ranked as less cost-effective measures due to high life cycle cost. In general, the results in the case study indicate that the appliance replacement is more cost-effective than the building envelope replacement. Theoretically, it is desirable for all suitable retrofit measures to be applied so as to achieve maximum energy savings in existing buildings. However, the priorities in retrofit could be influenced when cost-effectiveness is taken into consideration. The sensitivity analysis studied the effects of electricity price and discount rate on retrofit measures ranking. The results show that the electricity price can result in different cost-effectiveness values. However, the change of the electricity price does not alter the ranking results. This is reasonable as the ranking results are generally not expected to be significantly influenced by electricity prices. The discount rate has changed the ranking results in this case study. Since the choice of discount rate is usually made as a business decision (or potentially consumer decision in this case), the change in the ranking results could shed light on the needed changes in business decision when the retrofit project is under a fixed budget.

5.4. Discussion The ranking results summarized in Table 3 indicate that the rank sequence obtained based on energy savings only has been significantly altered when cost-effectiveness is used as the ranking criteria. It has been shown that lighting replacement is the best choice in terms of costTable 4 Ranking results for different electricity price scenarios. Measures

Lighting replacement Air-conditioner replacement Refrigerator replacement Clothes dryer replacement Clothes washer replacement Water heater replacement Wall retrofit Window replacement Roof retrofit

Reference scenario

Low electricity price

High electricity price

Cost effectiveness (US$/kwh)

Ranking

Cost effectiveness (US$/kwh)

Ranking

Cost effectiveness (US$/kwh)

Ranking

0.047 0.258 0.304 0.322 0.522 0.990 2.186 3.099 8.792

1 2 3 4 5 6 7 8 9

0.047 0.259 0.306 0.323 0.524 0.991 2.194 3.106 8.799

1 2 3 4 5 6 7 8 9

0.047 0.257 0.300 0.320 0.520 0.988 2.173 3.086 8.779

1 2 3 4 5 6 7 8 9

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Table 5 Ranking results for different discount rates scenarios. Measures

Lighting replacement Air-conditioner replacement Refrigerator replacement Clothes dryer replacement Clothes washer replacement Water heater replacement Wall retrofit Window replacement Roof retrofit

Reference scenario

Low discount rate

High discount rate

Cost effectiveness (US$/kwh)

Ranking

Cost effectiveness (US$/kwh)

Ranking

Cost effectiveness (US$/kwh)

Ranking

0.047 0.258 0.304 0.322 0.522 0.990 2.186 3.099 8.792

1 2 3 4 5 6 7 8 9

0.032 0.177 0.157 0.200 0.326 0.662 0.872 1.261 3.687

1 3 2 4 5 6 7 8 9

0.077 0.454 0.777 0.652 1.068 1.861 9.086 12.678 35.084

1 2 4 3 5 6 7 8 9

6. Conclusion

Beyond the results obtained in this study, the proposed method can help identify gaps in the existing legislations in building energy performance and standards taking into consideration cost-effectiveness in improving building energy efficiency. This method can also help building owners and energy service companies identify cost-effective measures in improving building energy performance without having to engage in an elaborate building energy simulation effort. Thus it would be ideal to have a direct linkage between the metamodel and the life cycle cost assessment methods to achieve simultaneous and potentially automated parameter calibration, and ranking and selection of measures by cost-effectiveness. We recommend the development of such direct integration as future research. This metamodel has not considered climate and building parameters which could pose significant influence on building energy performance. The next recommended future work is to develop more comprehensive metamodel to consider various different input parameters. In addition, the metamodel based method is used to evaluate retrofit measures for existing buildings. This method can also be extended to evaluate alternative design for energy efficiency scenarios for new buildings. Thus a third and final recommended future work is to adapt the method to evaluating new buildings based on design files and climatic conditions.

In this study, an integrated energy and economic assessment method for ranking and selecting the cost-effective retrofit measures is developed. This method is developed through soft-linking a metamodelbased Bayesian method and a life cycle cost assessment method. The Bayesian model is used to simultaneously calibrate and rank parameters used in building energy simulation models. This can complement the use of building energy simulation models in obtaining more reliable energy performance simulation results through parameter calibration as the method can account for various uncertainties in the calibration procedure. Among the various applications of the method, the metamodel method can be used to rank among a selection of retrofit measures based on energy savings. Building owners can decide the priorities such as improving chiller EER, building envelope, and equipment efficiency based on the ranking results. As a contribution to the literature in the development and application of metamodel based method in ranking and selecting retrofitting measures, a life cycle cost assessment method is soft-linked to the metamodel method. The life cycle assessment method takes into consideration investment, operating and maintenance, electricity, and other important cost components to compute the total cost of retrofit. Three indicators are used to compare the ranking and selection results, namely, total energy savings, total cost of retrofit, and cost-effectiveness (measured by dollars per kilowatt-hour of energy saved or $/kWhsaved) which can all be directly computed from the integrated method proposed in this study. Ranking of measures based on cost-effectiveness measured $/kWhsaved instead of just energy savings is an important addition to the literature because all retrofit projects are under fixed budget which requires business decisions to prioritize the implementation of measures. A case study is developed to validate the method and demonstrate its advantages. Key findings are summarized as follows:

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1. Incorporating the cost-effectiveness consideration into the model can significantly change the ranking results based on only energy saving. For example, wall and window retrofit are ranked the highest based on energy savings and lowest based on cost. As a result, wall and window retrofit measures are ranked 7th and 8th by cost-effectiveness. 2. Electricity price has very little influence on the cost-effectiveness ranking of retrofit measures mainly because electricity price has the same influence the total cost of all appliances. Due to the significant difference in the upfront cost between envelope retrofit measures and appliances, the electricity price is unable to significantly influence the ranking results between appliances and envelope related measures. 3. From the observation of the computational resources, metamodel based method is much more efficient and accurate in estimating building energy consumptions as compared to the use of physical building data in engineering-based simulation models. 8

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