J. Cent. South Univ. (2012) 19: 803-808 

DOI: 10.1007/s11771-012-1075-5

Weighting indicators of building energy efficiency assessment

taking account of experts’ priority

YANG Yu-lan(杨玉兰), TAI Hui-xin(邰惠鑫), SHI Tao(施韬)

College of Civil Engineering and Architecture, Zhejiang University of Technology, Hangzhou 310014, China

? Central South University Press and Springer-Verlag Berlin Heidelberg 2012

Abstract: Analytic hierarchy process (Group AHP) is combined with two different methods of assigning experts’ priority to weight indicators in building energy efficiency assessment. One is to assign the experts’ priority averagely, and the other is to use cluster analysis to assign experts’ priority. The results show that, 1) Different expert’s priority assigns result in great different weights of indicators in building energy efficiency assessment, therefore, the method of assigning experts’ priority should be taken into account carefully while weighting indicators of building energy efficiency assessment using Group AHP; 2) Three indicators are found to be overwhelmingly important in residential building energy efficiency assessment in the hot summer and cold winter zone in China. They are ‘Outdoor & indoor shadow’, ‘Heating & air-conditioning facilities’ and ‘Insulation of envelope’; 3) The method combining cluster analysis with Group AHP to weight indicator of building energy efficiency assessment has the advantage of finding overwhelming important indicator, whereas, some less important indicators have a tendency to be ignored. A useful reference is provided for building energy conservation including policy revision and energy efficient residential building design.

Key words: energy efficiency; building; weighting; cluster analysis; analytic hierarchy process

1 Introduction

It is undoubted that building energy efficiency assessment is one of the key measures to build energy conservation. Some building energy efficiency assessment policies have been proposed in the past decades in China. For example, ‘The Technical guides for building energy efficiency testing and labeling’ [1] assesses the building energy efficiency based on the building energy simulation results, however, computer simulations for energy consumptions in buildings are suitable for using in the design stage but it is not capable of comprehensively assessing the energy efficiency [2]. In addition, the code of ‘Evaluation standard for green building (GB 50378—2006)’ [3] and the code of ‘Technical standard for performance assessment of residential buildings (GB/T 50362—2005)’ [4] assess building from a relative wide cope beyond energy efficiency, therefore, it is not suitable to assess building energy efficiency directly yet. ‘The Evaluation standard for energy efficient buildings (GB/T 50668—2011)’ [5] provides method of assessing the energy efficient building, however, its indicators are not weighted.

Internationally, there are many building environmental or green building assessment methods, such as the Leadership in Energy and Environmental Design (LEED) [6], and the British Research Establishment Environmental Assessment Method (BREEAM) [7]. However, these methods assess building from the perspective of the impact and sustainability of the built environment. It is also reported that a reduction in energy use is not necessarily the case for every ‘green building’ [8]. Therefore, they are not suitable to assess and thereafter to label energy efficiency in buildings.

The current study for identifying indicators of building energy efficiency assessment methods in China has diverse focuses. One proposed indicator list focuses on building envelopes [9] while the other focuses on economics [10]. Seventeen indictors [11] are proposed based on the ‘Design standard for energy efficiency of residential buildings in the hot summer and cold winter zone (JGJ 134—2001)’ [12], again focusing on building envelopes. A proposed indicator list does not include the factors such as ‘indoor thermal environment’ and ‘building operation and management’ which are the main aspects of building energy efficiency assessment [13]. Furthermore, the above identified indicators are not weighted.

Therefore, it is essential to identify indicators and assign weights to identified indicators, since the role of the weight serves to express the importance of each indicator relative to the others in a quantitative way. A method has been proposed to identify indicators. The group analytic hierarchy process (group AHP) is used to weight these identified indicators for assessing the building energy efficiency [14], nevertheless, experts’ weight is not taken into account.

A method is proposed to assign priority for identified indicators for building energy efficiency assessment by combining the Group AHP and cluster analysis. Group AHP is used to weight the identified indicators, while cluster analysis is employed to assign the priority to experts. Group decisions have many merits such as more fair judgment and tackling extreme individual [15-16]. Assigning experts’ priority plays a key role in group decision, since experts vary in many facts such as education background, professional experience, individual preference, and awareness of topic. Using cluster analysis to assign the priority of experts is regarded as a convincing method at present [17-18].

The proposed method is applied to weight identified indicators of energy efficiency assessment in residential buildings in the hot summer and cold winter zone in China. Two ways of assigning experts’ priority are carried out to investigate how the experts’ priority makes impact on the weights of indicators of building energy efficiency. One is to assign the experts’ priority averagely, and the other is to use cluster analysis to assign experts’ priority. Both are combined with Group AHP to weight indicators in residential building energy efficiency assessment in hot summer and cold winter climate zone in China. It is our expectation that the proposed method and the weighted indicators in building energy efficiency assessment provide reference to policy making and revising.

2 Method of weighting indicators of building energy efficiency assessment

AHP can be applied in both individual and group decision making context [19]. Two different methods of assigning experts’ priority are combined with Group AHP to weight indicators of building energy efficiency assessment.

2.1 Method of using group AHP to weight indicators with assigning experts’ priority averagely

The implement of this method consists of three main steps:

Step 1: Construction of individual comparison matrices from the questionnaire survey

In this step, a questionnaire survey involving acknowledged experts has been carried out to attain individual comparison matrices.

We have contacted the individual experts separately to get the filled questionnaire. Supposing that there are m filled questionnaires to weight n indicators, the individual comparison matrix from the expert e can be given as

                       (1)

where A_e is the individual comparison matrix from the expert e, e= 1, 2, …, m;  is the relative importance between indicator i and indicator j based on the judgement of expert e, the value referenced to the nine-point method recommended by Saaty, .

Step 2: Synthesis of individual comparison matrices to produce a group comparison matrix

Individual comparison matrices are synthesized to group comparison matrix by the geometric mean of the individual comparison matrices. The detailed calculation can be seen in Eq. (2):

A=   (2)

The vector U=(u₁, u₂,…,u_n) relating to the maximum eigenvalue of A implies the weight of the indicators. A is the group comparison matrix and there are m experts in the group.

Step 3: Consistency estimation

The consistency estimation must meet the require- ment of AHP; otherwise, a new comparison matrix is needed to be reconstructed to weight the indicators.

2.2 Method of using group AHP to weight indicators with cluster analysis to assign experts’ priority

The implement of this method consists of ten main steps:

Step 1: Construction of individual comparison matrices from the questionnaire survey.

This is same as Eq. (1).

Step 2: Each individual expert is regarded as a group. i.e. G₁={Expert 1}, G₂={Expert 2}, …, G_m={Expert m}, and there are m experts in survey. In addition, one variable q is introduced and q=m.

Step 3: Calculation of d_ij, which indicates how close the assessment of expert i and expert j, i=(1, 2, …, m), j=(1, 2, …, m). U_e=(u_e1, u_e2,…, u_en)(e=1, 2, …, m) is the vector relating to the maximum eigenvalue of A_e. d_ij can be calculated by

                  (3)

Step 4: Supposing d_xy is the maximum of d_ij, group G_x and G_y are united to yield a new expert group G_q+1, G_q+1={ G_x, G_y }.

Step 5: If q=2(m-1) or meet the close restriction of cluster analysis, then go to Step 7, else go to Step 6.

Step 6: G_x and G_y are taken out from groups, and G_q+1 is added into groups for further cluster analysis:

d_i,q+1=max{d_ix, d_iy}, i≠x, y, i=1, 2, …m

And q=q+1, then go to Step 4.

Step 7: Drawing the cluster analysis, experts are divide into couple groups.

Step 8: Assigning experts’ priority

It is supposed that m experts are divided into l group, and there are ψ_j experts in group j. The priority of group is calculated by

                     (4)

And then the group weight is assigned to each expert in this group averagely.

Step 9: Weighting indicators

Individual comparison matrices are synthesized to group comparison matrix by

A=

(15)

where β_i (i=1, 2, …, m) are the weights of expert i, .

The vector U=(u₁, u₂, …, u_n) relating to the maximum eigenvalue of A implies the weight of the indicators.

Step 10: Consistency estimation

The consistency estimation must meet the requirement of AHP; otherwise, a new comparison matrix is needed to be reconstructed to weight the indicators.

3 Weighting indicator of residential building in hot summer and cold winter area in China

3.1 Identification of indicators of residential building in hot summer and cold winter area in China

Indicators identification plays a key role in building energy efficiency assessment. Seventeen indicators have been identified and are listed in Table 1. A method which combines a wide-ranging literature review and an expert survey as well as analysis has been implemented to achieve these indicators.

Table 1 Indicators of energy efficiency assessment in residential building in hot summer and cold winter zone in China [14]

3.2 Weighting indicators of residential building in hot summer and cold winter area in China by using Group AHP combined with assigning experts’ priority averagely

A questionnaire survey which aims to weight the above 17 indicators in the hot summer and cold winter zone in China has been conducted in experts in building energy efficiency assessment. Thirty filled questionnaires are effective. Consistency estimation has not only met the requirement of AHP but also validated the sample size sufficiently. The weights of 17 indicators are listed in Fig. 1.

3.3 Weighting indicators of residential building in hot summer and cold winter area in China by using Group AHP combined with cluster analysis to assign experts’ priority

Cluster analysis is used to assign priority for experts involved in survey. The cluster analysis process is mapped in Fig. 2 by carrying out steps 2-7 in Section 2.2. For example, for the first cluster analysis, d_19,24=0.97 is found as the maximum of d_ij, therefore, the expert 19 and expert 24 are put into a group, and the experts 14, 20 and 23 are put into this group in the next cluster analysis steps. Figure 2 shows that the process has 22 clustering steps in total. From Fig. 2, we can see d_ij=0.80 is a reasonable close restriction of cluster analysis    process. Finally, 30 experts are divided into 9 groups by clustering analysis as follows:

G₁={19, 24, 14, 20, 23}

G₂={1, 9}

G₃={5, 6, 21, 12}

G₄={11, 26}

G₅={10, 27, 30, 13, 28, 7, 29, 8, 3}

G₆={2, 17, 4, 15}

G₇={16, 22}

G₈={18}

G₉={25}

Fig. 1 Weights of indicators of residential building energy efficiency assessment in hot summer and cold winter zone by using Group AHP combined with assigning experts’ priority averagely [6]

Fig. 2 Cluster analysis process

The nine groups are assigned weights as Fig. 3 by implement of Step 8 in Section 2.2. Then, the priority of a group is assigned to its expert members averagely.

These 17 indicators are weighted by Eq. (5) in Step 9 in Section 2.2. The weights of these indicators are listed in Fig. 4.

Fig. 3 Clustered group weights

3.4 Discussion

There is great difference between Fig. 1 and Fig. 4. It is very clear that different methods of assigning experts’ priority yield great difference in weights of indicators in residential building energy efficiency assessment in hot summer and cold winter zone in China. Therefore, the method of assigning experts’ priority should be taken into account carefully while weighting indicators of building energy efficiency assessment using group AHP. From Fig. 1, we can see the following facts:

1) ‘Heating & air conditioning facilities (C1)’ has the maximum weighting of 0.13, i.e. ‘Heating & air conditioning facilities’ is the most important indicator of residential building energy efficiency assessment in the hot summer and cold winter zone in China

2) ‘Insulation of envelope (B1)’, ‘Indoor thermal humid environment (E1)’ and ‘Indoor air quality (E3)’ are the three important indicators due to the fact that they have weights of 0.10.

3) ‘Water supply facilities (C3)’ and ‘Lifts     (C4)’ share a weight of 0.02. This means both of these indicators are the less important indicators in residential building energy efficiency assessment in the hot summer and cold winter zone in China.

Fig. 4 Weights of indicators of residential building energy efficiency assessment in hot summer and cold winter zone by using Group AHP combined with cluster analysis to assign experts’ priority

From Fig. 4, we can see the following facts:

1) ‘Outdoor & indoor shadow (B3)’ has the maximum weight of 0.28, and ‘Heating & air conditioning facilities (C1)’ has the weight of 0.25, i.e. ‘Outdoor & indoor shadow’ and ‘Heating & air conditioning facilities’ have overwhelming importance in residential building energy efficiency assessment in the hot summer and cold winter zone in China.

2) ‘Insulation of envelope (B1)’ is the important indicator because it has a weight of 0.09 .

3) The remaining indicators have weights less than 0.05, which means they are less important indicators in residential building energy efficiency assessment in the hot summer and cold winter zone in China.

4 Conclusions

1)  Weights of indicators show great difference by using different expert’s weights assigning method. This means the method of assigning experts’ priority should be taken into account carefully while weighting indicators of building energy efficiency assessment using Group AHP.

2) Three indicators are found to be the most important in residential building energy efficiency assessment in the hot summer and cold winter zone in China. They are ‘Outdoor & indoor shadow’, ‘Heating & air conditioning facilities’ and ‘Insulation of envelope’.

3) Cluster analysis is combined to Group AHP to weight indicator of building energy efficiency assessment, which has the advantage of finding overwhelming important indicator, whereas, some less important indicators are tended to be ignored.

4) Group AHP has been used to weight indicators for assessing the energy efficiency of residential buildings in China, nevertheless, experts’ weight is not taken into account. Therefore, this study provides improvement for the application of Group AHP in weighting building energy efficiency assessment.

5) The current ‘Evaluation standard for energy efficient buildings (GB/T 50668—2011)’ in China proposes a method to assess energy efficient building, nevertheless, it does not weight its indicators. Therefore, this work provides a useful reference for policy revision of energy efficiency assessment in buildings in China.

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(Edited by YANG Bing)

Foundation item: Project(2010R10036) supported by the Science and Technology Department of Zhejiang Province, China

Received date: 2011-07-26; Accepted date: 2011-11-14

Corresponding author: YANG Yu-lan, Associate Professor, PhD; Tel: +86-571-88871067; E-mail: gzyyl@126.com