Abstract
besos: Building and Energy Simulation, Optimization and Surrogate Modelling
Highlights
The Python library besos along with its associated web-based platform BESOS help researchers and practitioners explore energy use in buildings more effectively. This is achieved by providing an easy way of integrating many disparate aspects of building modelling, district modelling, optimization and machine learning into one common library
Figure 1: analysis domains encompassed by BESOS
The second key contribution of the besos library is in facilitating surrogate modelling, where machine learning models are trained on many samples of simulation input and output data so that the resulting model can be used as a fast but approximate surrogate for the computationally-intensive simulation
Summary
The buildings sector is one of the largest contributors to CO2 emissions, comprising up to 33% of the global total (ürge-Vorsatz et al, 2007). Improved computational methods are needed to help design more energy-efficient buildings. The Python library besos along with its associated web-based platform BESOS help researchers and practitioners explore energy use in buildings more effectively. This is achieved by providing an easy way of integrating many disparate aspects of building modelling, district modelling, optimization and machine learning into one common library. Figure 1: analysis domains encompassed by BESOS
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