ZEMCH 2015 - International Conference Proceedings | Page 328

The strength of the relationship between the number of IBTs used in a building and the percentage BREEAM / LEED score obtained by the building can be summarised by the coefficient R. According to Cohen( 1988) magnitudes 0.10, 0.30 and 0.50 correspond roughly to relations that are considered small, medium and large respectively. An R value( Pearson Correlation) of 0.759( Table 2) and 0.748( Table 3) signals a very strong correlation between the variables, one that is highly significant. The fact that the sign of R is positive indicates that as the number of IBTs used in a building increases, the value of the BREEAM and LEED score increases as well. The R2 value is a standardised coefficient, which ranges from 0 to 1; 1 indicates a perfect fit of the data points to a straight line and 0 indicates the worst possible fit. An R2 value of 0.576( Table 2) and 0.559( Table 3) suggests a high number of data points that fit the trend line.
Table 2: Model Summary BREEAM Case Studies( refer to Figure 4)
Model R Value R2 Value Adjusted R2 Value Std. Error of the Estimate
1. 759a. 576. 562 8.51692
Table 3: Model Summary LEED Case Studies( refer to Figure 5)
Model R Value R2 Value Adjusted R2 Value Std. Error of the Estimate
1. 748a. 559. 497 12.06162
Additionally an ANOVA test of significance was carried out to show whether the R2 value for the relation between the two variables is significant. Since in this case the value of Significance is 0.000( Table 4) and 0.020( Table 5), which is less than 0.05, the relation between the two variables is significantly different than zero, meaning the R2 value is highly significant.
Table 4: ANOVA Test of Statistical Significance( BREEAM)( refer to Figure 4)
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
2861.747
1
2861.747
39.452
. 000b
Residual
2103.600
29
72.538
Total
4965.347
30
Table 5: ANOVA Test of Statistical Significance( LEED)( refer to Figure 5)
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
1293.211
1
1293.211
8.889
. 020b
Residual
1018.378
7
145.483
Total
2311.589
8
a. Predictors:( Constant), No. of Intelligent Building Technologies b. Dependent Variable: Percentage BREEAM Score
3.3 Overview After the analysis it was observed that the number of IBTs in a building positively affects its sustainability rating. Some related findings about the impact different type of IBTs have on the BREEAM / LEED Scores are as follows:
• Highly integrated and interactive IBTs such as building management, energy management and facility management systems were predominantly found in buildings with a high Sustainability Rating( EXCELLENT, OUTSTANDING, GOLD and PLATINUM).
• Case studies with BMS and integrated systems, which shared data and interacted with other
326 ZEMCH 2015 | International Conference | Bari- Lecce, Italy