in Fig. 2.
The stakeholder defines the simulation objectives and the relevant
performance indicators. Based on this information, the checklist
framework can be used to identify the entities and variables to be used
in the simulation and thus estimate the initial modeling complexity.
The initial modeling complexity should be the lowest possible
complexity that satisfies the simulation objectives in terms of
performance indicators. The quantification of validity of the initial/
minimum required modeling complexity is achieved by specifying a
range for error tolerance, as a model deviation of the real world.
The error in a verifiedmodel is the sumof: (i) abstraction error, (ii)
input data error, and (iii) numerical errors. Here, only the former two
are discussed while it is assumed that by decreasing the discretization
step the numerical errors can be controlled. The first error is due to the
modeling abstractions, i.e. using an incomplete model of a physical
system, and the second is due to uncertainties in the parameters
themselves. Sometimes the distinction between the two is not clear.
Parameter uncertainty can be quantified and therefore the corre-
sponding uncertainty of the model output as well. This uncertainty of
the output is known as predictive uncertainty.
The modeling uncertainty is not easily quantifiable and therefore
its influence can be considered as a modeling bias. As illustrated in
Fig. 3, with the increase of modeling complexity the predictive
uncertainty rises as there are more parameters to consider. On the
other hand, the models approach reality and the bias decreases. The
curve that defines predictive uncertainty depends on how much ofFig. 2. Schematic representation of a checklist rationale [55]: 1. There must be a total
tracking of items in the requirements to the conceptual model. 2. There should be a
specific simulation element for every item (parameter, attribute, entity, task, state,
etc.). 3. As far as possible, there should be “real world” counterparts for every
simulation element. 4. The simulation elements should correspond to standard and
widely accepted decomposition paradigms to facilitate acceptance of the conceptual
model and effective interaction (including reuse of algorithms and other simulation
components) with other simulation endeavors. 5. Simulation elements required for
computational considerations that fail tomeet any of the previously stated items should
be used only when absolutely essential. 6. There should be no extraneous simulation
elements.关键词:
建筑系统性能仿真,暖通空调性能仿真,集成建筑性能模拟,暖通空调技术仿真,建模和仿真方法
文摘本文概述了空调(HVAC)建模系统的加热、通风以及暖通空调系统的设计和分析。介绍了每个分类并分别举出实例切合实际的说明了暖通空调建模的性能。本文总结了一般情况下用于建模(i)暖通空调组件,(ii)暖通空调的方法。为了便于控制(iii)暖通空调系统，文章给出解决方案即针对目标空调系统选择建模。总而言之，一个人应该针对要仿真的目标选择一种暖通建模方法。
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