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Building retrofits are typically guided by analyses through building energy simulation models.
Building energy simulation models attempt to capture these perturbations as accurately as possible.
BESMs (building energy simulation models) play an important role in the design, optimization and retrofit of buildings.
In this paper, a metamodel-based Bayesian method is proposed to simultaneously calibrate and rank input parameters to building energy simulation models.
Architects and engineers rely on building energy simulation models to calculate the energy consumption of buildings and to assist them in making design decision.
However, none of the building energy simulation models is immune to the influence of uncertainties.
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These models are: spectral optical model, computational fluid dynamics model and building energy simulation model.
Physics-based whole building energy simulation modeling is the most common approach.
The research methodology gathered data on-site (surface temperatures and reflectivity) and used this in conjunction with the as-built drawings to construct a building energy simulation model.
The building energy use was estimated using a GIS-based building energy simulation model in conjunction with Department of Energy/Energy Information Administration survey data, the Assessor's parcel data, GIS floor areas data, and remote sensing-derived building height data.
This paper presents a method for developing a probabilistic-based occupancy model that focuses on occupants' long vacancy activities (greater than 1 week) and other potential building underutilization that is further integrated with a building energy simulation model.
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