Rock Street, San Francisco

The intention with this simulation was to see how the
parametrization of a window can affect the energy cost. Three heights were
compared while the width of the window was fixed. The model of the house was
constructed in such a way that no other energy sources were considered, such as
heating or cooling sources. This was done to ensure that the sun light was
supplying source and to not complicate the evaluation of the results. The
analyzed window was fixed on a 450 mm width 34 with the height dynamically
changing from 2000 mm, 3000 mm and 4000 mm. The approximate result changes in
energy cost can be viewed in the graph figure 4,2. Figure 4.2: Window Height
The graph shows a linear increase in energy cost when choosing a higher window
due to transmission losses, which was expected and is an indicator that the
dynamo script works. What can also be seen in the graph above is that the
increase in energy cost is not proportional to the increase of height, i.e. the
increase from the 2m-3m height does not give similar energy cost as 3m-4m. This
is because the energy balance in the house depends not only on the transmission
losses, but also additional energy gathered from the sun. The additional height
of the window compensates the energy losses by giving the slope a flatter
character. The final evaluation of the results shows that choosing a higher
window height is the least favorable in this case. 4.1.2 Width Parametrisation
How will the energy distribution and price be affected by the changes of Width?
In the case of parametrization of the window width, the height was now used as
a fixed value with a dynamically changed width. As shown in the figure 4.3, as
expected, the energy usage was increasing in relation with the window width. It
is worth noticing that even though the window area on every iteration is the
same as 35 with case of window height parametrization, the energy usage is not
similar. This can be explained by the orientation of the window and the
different ways the sunlight is transmitted to the building. Compare for example
the energy usage for the cases with 450×200 and 2000×450 window dimensions.
Though they are being same the resulting energy usage differs. This is a second
indication that the written dynamo code is working and that the Dynamo tool can
be suited in these kind of energy simulations. Figure 4.3: Window Width The
analyzed window was fixed on a 450 mm height, with a dynamically changing width
from 2000 mm, 3000 mm and 4000 mm. The approximate result changes in energy
cost can be viewed in the graph figure 4.3.