Advanced Building Simulation

Chapter 3
Simulation and uncertainty
Weather predictions
Larry Degelman
3.1 Introduction
Everyone deals with uncertainty every day—whether predicting the outcome of an
election, a football game, what the traffic will be like or what the weather will be.
Most of us have become accustomed to erroneous predictions by the weather
forecasters on television, but we seem willing to accept this sort of uncertainty. No
forecaster will give you 100% assurance that it will rain tomorrow; instead, they will
only quote to you a probability that it will rain. If it doesn’t rain the next day, we usually
conclude that we must have been in the “nonprobable” area that didn’t receive
rain; we don’t usually sue the weather forecaster. This type of prediction is done by
computerized simulation models, and in fact, these simulation models are not
intended to produce one specific answer to a problem. Rather, the underlying premise
of simulation is that it discloses a range of situations that are most likely to occur
in the real world, not necessary a situation that will definitely occur. This is a very
useful aspect to a building designer, so as not to be confined to a single possibility. In
short, simulation allows you to cover all the bases.
When we use simulation models to predict thermal loads in buildings, we should
recognize that there would be built-in uncertainties due in part to the weather data
that we use to drive the simulation models. Most forecasters agree that the best predictor
of weather conditions is the historical record of what has occurred in the past.
The same forecasters, however, would agree that it is very unlikely that a future
sequence of weather will occur in exactly the same way that it did in the past. So,
what kind of weather can be used to drive energy simulation models for buildings?
what most simulationists would like to have is a pattern of “typical weather”? This
entails finding (or deriving) a statistically correct sequence of weather events that
typify the local weather, but not simply a single year of weather that has happened in
the past.
In this chapter, a simulation methodology is introduced that is intended for application
to the climate domain. Featured is the Monte Carlo method for generating
hourly weather data, incorporating both deterministic models and stochastic models.
Overall, the simulation models described here are targeted toward synthetic generation
of weather and solar data for simulating the performance of building thermal
loads and annual energy consumption. The objective is not to replace measured
weather with synthetic data, for several reliable sources already exist that can provide