Advanced Building Simulation

62 Degelman
Simulation of a building’s energy performance is a way to help designers calculate
life cycle costs and thus optimize the building’s ultimate cost and performance.
Simulations of this sort are routinely being accomplished every day. The major driving
mechanism of thermal heat flows in buildings is the climate. All computer programs
require input of extensive weather and solar radiation data, usually on an
hourly basis. If these data are readily available, there is no need to simulate the
weather; however, when the hourly data are lacking, there is a bonafide need for simulated
weather sequences. Even if hourly weather data are available, sometimes it
might only represent a few years of record. Such a short record is only anecdotal and
cannot purport to represent long-term “typical” weather. The only way to represent
the full spectrum of weather conditions that actually exist is to collect data from
many years (ten or more) of hourly weather data. Very few weather sites have reliable
contiguous weather data available for extremely long periods of time. If they do
have the data, it usually has to be reduced to a “typical” year to economize in the
computer run time for the simulations. In addition, users can be frustrated over
frequent missing weather data points.
This situation can be elegantly addressed by a model that generates hourly
weather data for any given location on the earth. There are never any missing data
points and reliable predictions can be made of peak thermal load conditions as well as
yearly operating costs. This chapter presents such a model. The variables in this simulation
technique are kept as basic as possible so that the technique can be applied to
estimating heat gains and losses in buildings at any location on earth where scant
weather statistics are available. The calculations that establish the actual heat gains
and heat losses and the air conditioning loads are not described here, but these methods
can be found in other publications (Haberl et al. 1995; Huang and Crawley 1996;
ASHRAE 2001).
Establishing “typical” weather patterns has long been a challenge to the building
simulation community. To this date, there are various models: for example, WYEC
(Weather Year for Energy Calculations) (Crow 1983, 1984), TRY (Test Reference
Year) (TRY 1976), TMY (Typical Meteorological Year) (TMY 1981), TMY2 (Stoffel
1993; TMY2 1995), CWEC (Canadian Weather for Energy Calculations), and IWEC
(International Weather for Energy Calculations). None of these models are based on
simulation; rather, they are based on meticulous selections of typical “real weather”
months that make up a purported “typical year.” These models should be used if they
are available for the locale in which the building is being simulated; however, an alternative
approach (i.e. synthetic generation) is called for when these weather records
are not available.
3.3 The Monte Carlo method
One approach that can be used to simulate weather patterns is use of a random
sampling method known as the Monte Carlo method. The Monte Carlo method
provides approximate solutions to a variety of mathematical problems by performing
statistical sampling experiments on a computer. The method applies to problems
with no probabilistic content as well as to those with inherent probabilistic structure.
The nature of weather behavior seems to be compatible with this problem
domain.