Civil Engineering Reference
In-Depth Information
perform better design, building owners and energy contractors can have a more
realistic energy cost prediction of their buildings, and code and standard writers can
use the results in order to set meaningful barriers for achieving buildings energy
effi ciency goals.
As I said earlier, an uncertainty analysis is a procedure that helps the architect
and engineers to realize the range, probability distribution and fl uctuation of their
model output (consumed energy) when they change the inputs to the model. They
gain multiple benefi ts from the uncertainty analysis which among them the two
most effectives are, making better decisions by better understanding the system and
opening ground for discussion and communication for system improvement.
Also as it was said earlier, a complementary analysis to uncertainty evaluation is
sensitivity analysis which is a method that helps to pin down the uncertain param-
eters which have the most infl uence on the simulation outputs. The most important
benefi t of this type of analysis is that after the most infl uential parameters are identi-
fi ed, higher level of attention can be focused on them to improve the outcome of
model and therefore the design.
In past two decades, ASHRAE standard (ASHRAE
2013
) in its “G” section has
presented a modeling solution for comparison between a design building and an
imaginary base-building similar to the design building with pre-determined HVAC
systems along with other prescribed requirements. To comply with requirements of
this standard and also in order to achieve LEED points for the design building, the
energy cost of the design building model should be compared to the energy cost of
the base-building. The higher the savings or the difference between the two out-
comes is a higher number of LEED points in Energy and Atmosphere (credit 1)
section will be achieved. In spite of the fact that implementing this solution has
made an obvious positive impact on the achieved degree of savings on the energy
consumption of the newly designed buildings, it can be argued that there is still
room for improving this method for achieving even higher levels of energy savings
by comprising the uncertainty measures as part of the modeling inputs. It also can
be conferred that factoring uncertainty in this solution can prevent those buildings
that are not really designed with effective energy saving measures from being certi-
fi ed as energy compliant buildings, or at least not to record high sustainability and
effi ciency scores. Of course that itself can be translated to even more net energy
savings.
Generally when energy modelers use commercially available simulation soft-
ware to perform a building energy modeling simulation, they enter numerous inputs
into both design building and base-building models according to guidelines of
ASHRAE standard (ASHRAE
2013
). These inputs cover a wide range of different
categories such as location of the building, building envelope material sizes and
heating characteristics, internal loads, and HVAC system. Each of these inputs car-
ries its associated quantity of uncertainty which if it is ignored (as it is being ignored
in current practice) will reduce the reliability of the outcome of the modeling simu-
lation exercise. To overcome this shortfall, in recent years as it was noted earlier in
this chapter relatively large number of researchers have attempted to include the
