COMPARISON OF REGRESSION TECHIQUES FOR SURROGATE MODELS OF BUILDING ENERGY PERFORMANCE
Surrogate modeling is a technique to approximate the
behavior of complex systems based on a limited set of
computationally expensive simulations. Such models
can permit real time optimization of building designs.
There are many ways to develop the surrogate model
however, the relative advantages and disadvantages of
one modeling technique versus another are not well
understand, so modelers lack a criteria for selecting
amongst approaches. The objective of this paper is to
compare the accuracy and computation time of multiple
methods. This paper also explores the choice of design
variables and parameter combinations to estimate the
building energy consumption when climate is one of the
variables. The results of this analysis will contribute to
selecting a regression technique that limits errors
relative to a detailed simulation.
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