Besos

Faure, Gaëlle; Christiaanse, T.V.; Evins, Ralph; Baasch, Gaby

doi:10.1145/3360322.3360995

Cited by 9 publications

(1 citation statement)

References 2 publications

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“…The BESOS Platform (Faure et al, 2019) provides executable Jupyter Notebooks (https: //jupyter.org/) via the Jupyter Lab interface that implement many of the features of the besos library in a web-interface with no set-up required. These notebooks mix executable code, visual outputs and formatted explanations, making them ideal for non-expert programmers who want to get on with running building energy models without navigating a huge codebase.…”

Section: Statement Of Needmentioning

confidence: 99%

besos: Building and Energy Simulation, Optimization and Surrogate Modelling

Westermann¹,

Christiaanse²,

Beckett³

et al. 2021

JOSS

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The buildings sector is one of the largest contributors to CO 2 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.

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Section: Statement Of Needmentioning

confidence: 99%

besos: Building and Energy Simulation, Optimization and Surrogate Modelling

Westermann¹,

Christiaanse²,

Beckett³

et al. 2021

JOSS

Self Cite

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Simulated Versus Monitored Building Behaviours: Sample Demo Applications of a Perfomance Gap Detection Tool in a Northern Italian Climate

Chiesa

Fasano

Grasso

2022

Innovative Renewable Energy

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Green building technologies and design-correlated choices may significantly contribute to supporting the transition toward net energy flows in the built environment. Nevertheless, large discrepancies are underlined between standard simulated and monitored building behaviours requiring approaches able to simply correlate real building behaviours and simulated ones to further support coherent certification and/or optimization. The paper focusses on the application of a semi-automatic methodology to compare and evaluate thermal behaviours of buildings considering monitored and simulated data. The approach is based on a new Python tool developed by the authors, able to manage EnergyPlus inputs and perform multi-source KPIs calculations. The mentioned tool is used here to support semi-automatic model verifications of real weather data by optimizing model parameters to fit monitored behaviours. The approach is applied in this chapter to two demo buildings, a municipality school and a residential unit, located in the Turin metropolitan area of Piedmont, in Northwest Italy.

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