Hence it can be inferred that to have an ‘ Intelligently-Sustainable ’ building , the level of building intelligence and sustainability has to be optimised taking into consideration the core function of the building and subsequently the occupants ’ immediate needs . This research has proved that one can achieve an intelligent and sustainable building but there is an optimisation level where one of the two will gain priority . This priority or the weighting value can be ultimately decided by the users ’ needs and the building ’ s functionality . In a building rating system every parameter has a weighted value which decides its relative importance among its peer parameters . Along with individual weighted values the ‘ Intelligently-Sustainable ’ building rating system can have an overall weighting system that determines which branch deserves priority so as to have an optimised level of intelligence and sustainability based on the users ’ needs and building ’ s function .
Thus the predictive model developed through this research will need to be further worked upon such that it not only includes the number and type of intelligent building technologies to determine the BREEAM or LEED score but also considers the users ’ needs and the buildings function . This will ensure that the final rating output by this system will be an optimised building that is optimally sustainable and intelligent . The new rating system with its consideration to users and functions will thus overcome the limitations of LEED and BREEAM that have attracted criticism .
Conclusions
The cross analysis of the qualitative data highlights the various benefits IBTs can provide in terms of energy savings ; water savings ; CO2 emissions ; improved productivity , reduced absenteeism , and safer & convenient spaces . The bivariate correlation analysis suggested that there is a strong positive correlation between the number of IBTs used in a building and the BREEAM and LEED score achieved by the building . The regression analysis and the 5-fold cross validation process has proved through multiple iterations that the logarithmic model is the best-fit model that describes the true relationship between the multi-dimensional entities- intelligent building technologies and sustainability scores . With the aid of this Predictive Model , this paper wants to introduce intelligence as a new kind of sustainability feature , and would argue that it needs to be added to the list of factors in Green Building Rating Systems that help calculate the sustainability value of a building .
The logarithmic model also helped infer the effect , different kinds of intelligent building technologies have on the sustainability value of a building . The findings from these led to a discussion about the importance of optimisation between building intelligence and sustainability based on factors such as user needs and building ’ s core function . This discussion along with the Predictive model generated through this paper will pave the way for a new building rating system that will cumulatively evaluate Building Intelligence and Sustainability .
References
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Building intelligence and sustainability using leed and breeam in the UK and Europe 331