Civil Engineering Reference
In-Depth Information
and probability of each option we can calculate the total utility scores of two sys-
tems to select the best possible choice between two selected systems, which of
course is the system with higher total utility score (see Figs.
13.7
and
13.8
). {When
you look at the partial utility score column you can see the maximum and minimum
costs in total cost column are correspondent with zero and one values, with maxi-
mum cost the least desirable and minimum cost the most desirable conditions.
Therefore the higher the fi nal utility value the more desirable condition you have}.
Of course in this case both systems resulted in almost identical utility scores. Now
assume we have changed the problem target from 5 to 10 years total cost estimate
(including the fi rst cost) (see Figs.
13.9
and
13.10
). This time the utility factor of
ground source heat pump is more clearly higher than traditional variable air volume
system. It shows when we change our target to a longer time horizon the ground
source heat pump will become more desirable, due to its lower energy
consumption cost.
We have used Eq. (
13.1
) because we are working with condition of “economic
bads” such as energy consumption, energy cost, etc. In conditions that we are deal-
ing with “economic goods” such as environment quality or savings we have to use
another version of utility function equation. Equation (
13.2
) is used for such
conditions:
(
)
/
Utility Function
=
Selected Attribute
-
Minimum Attribute
(
)
( 13.2 )
Maxim
um Attribute
-
Minimum Attribute
Equations (
13.1
) and (
13.2
) are proper when there is no risk tolerance included in
the decision maker's decision. Other versions of the utility function can be used for
conditions that the decision maker has some level of risk tolerance.
As the fi nal word I would like to say that there is an obvious need for improving
the way that building designers perform building energy modeling simulation. The
current deterministic modeling which is being performed via using the available
commercial energy modeling simulation software lacks the capability of factoring-
in the multiple uncertainties in the input elements into the energy consumption out-
put results. As a consequence considerable deviation in calculated energy
consumption could be translated to inaccurate scoring standards and therefore more
wasted energy than what it could be if the proper probabilistic calculations was
done. Therefore the main purpose of this section is to shed light on an important
missing factor from current energy modeling simulation and rating approaches and
bring the attention of the HVAC professional community of practice to the necessity
of making revisions to the current commercial energy modeling simulation software
based on a factor (uncertainty) which at this time is almost completely ignored from
their calculations. This approach can improve the process of decision making for
the design and construction of the buildings which if it is implemented would have
a considerable energy saving effect throughout the industry.
