OPTIMIZING THE USE OF REDUCED WEATHER SETS IN BUILDING ENERGY SIMULATIONS
In almost all cases, building energy simulations are
done with an entire year of weather data. As building
models become larger and simulation programs become
more complex, simulation runtimes have become an
issue, despite the overall improvements in computer
One obvious way to reduce runtimes is to not run the
simulation for an entire year, but for a subset of days
extracted from the year. The trade-off for the quicker
runtimes is of course a loss in accuracy. Previous
efforts using this technique have simply taken a random
sampling of the time series, such as doing the
simulation for four arbitrary 15-day periods spaced
equally from a “typical year” weather file.
This paper investigates using the same technique
developed for selecting the most representative months
making up a “typical year” weather files but applies it
to select a 7-day time series, i.e., a “typical week”, that
best matches the average long-term climatic conditions
of a month.
If the source weather data is a “typical year” weather
file, this technique analyzes the cumulative frequency
distributions (CFD) of temperature, solar, wind, etc., for
each of the 22-25 7-day series within that month,
compare them to the CFD for the full month, and then
selects the 7-day series with the smallest deviation in
the CFDs as the “typical week”. The twelve “typical
weeks” are then concatenated to produce a reduced
“typical year” weather file containing only 12, rather
than 52, weeks of data. For simplicity, the date stamp
for each 7-day series can be set to the middle of each
month. There are many simple ways to make such
reduced weather files compatible with existing
simulation program, although relatively modest changes
would be needed to simplify their use and speed up
runtimes by eliminating re-initialization between the
“typical weeks”. Once that is done, runtimes should be
reduced to 1/4 with little loss in accuracy compared to
running the full year.
If the source weather data is the historical time-series,
that should be used instead for selecting the “typical
week”, which would greatly expand the number of
candidate 7-day series by the number of years in the
time-series. In such instances, it has been found that
the reduced weather data set has an equal or at times
even better fidelity than the “typical year” file to the
average long-term climate conditions.
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