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    The major motive for the adoption of object-oriented software
    engineering approaches has been its support for modularity in
    modeling. However, a model for the simulation of a complex system,
    such as a building, in object-oriented languages is not trivial [47]. One
    of the main questions is to what the objects should correspond.
    Should they correspond to real-world entities, or to the equations
    associated with those entities. The lack of the agreement upon the
    above issue has resulted in a limited presence of object-oriented
    programming in the domain of BPS.
    3.4. Solution techniques for HVAC system simulation models
    The differences in solution techniques employed by different
    simulation tools are based on the distinction in the way the integrator
    is employed [48].
    Simultaneous modular solution, where the various components are
    integrated simultaneously by a common integrator. In general, the
    tools that employ this solution technique use model equations that
    are based on first principles [48]. Each component is described with
    time-averaged discretized heat and/or mass conservation statements,
    which are combined to form a system matrix, and which are solved
    simultaneously for each simulation time step using either an implicit,
    explicit or mixed numerical scheme.
    Independent modular solution, where eachmodule is providedwith
    inpidual integrator routines. In general, the tools that employ this
    solution technique use model equations that can be based on first
    principles but can also be empirical input/output correlations [48].
    The component's modules encapsulate all information relevant to the
    component's simulation model setting and execution. Each compo-
    nent is executed sequentially and the system solver iterates until a
    convergent solution has been found.
    Equation-based solution using formula manipulation, which has
    emerged in recent years with the developments of equation-based
    tools. Models composed with these tools cannot be executed directly.
    To be executed, a model needs to be transferred into a programming
    language that can be compiled. Tools employ different techniques to
    reduce the dimensionality of the linear and non-linear systems
    defined in the model in order to increase the execution efficiency of
    the compiled program. For example, in SPARK [49], mathematical
    graph algorithms are used for problem decomposition and reduction,
    greatly reducing solution time for wide classes of problems [50].
    4. Integration of building and HVAC system models
    The integration of building and HVAC system models is accom-
    plished at different levels. The models can be (i) sequentially coupled
    (many duct/pipe sizing tools, BLAST, DOE-2, etc.) – without system
    model feedback to the building model or (ii) fully integrated (ESP-r,
    EnergyPlus, IDA ICE, TRNSYS, etc.) – allowing the system deficiencies
    to be taken into account when calculating the building thermal
    conditions. Levels of detail of both building and system models can
    vary from simple (e.g. the bin method and pure conceptual
    representation for system model) to complex (numerical model of
    physical processes).
    5. Issues in selecting HVAC modeling approach
    Different HVAC system modeling approaches demand different
    levels of user skills, different modeling resolutions and details, and
    different levels of user customization capability. Higher explicitness in
    system representation requires more knowledge about the system
    because of the increasing number of model parameters for system
    specification, often difficult to obtain as they are not supplied by
    manufacturers. In addition, for higher explicitness in system repre-
    sentation the computational requirements become more intensive
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