Abstract
In the field of urban building energy models (UBEM), the significant mismatch between simulation results and energy metered data is one of the most important barriers when using them as a planning tool. Their implementation requires a great amount of detailed building-related data, which is not always available. These data could be collected or measured for a small group of existing buildings, however such detailed data collection effort becomes impractical for large urban areas. Therefore, UBEM require certain simplifications and assumptions which can distance the input data from reality, leading to a performance gap. In this sense, knowing the influence of all the input parameters on UBEM simulation results would allow to focus the data collection efforts on the inputs with the greatest sensitivity. In this context, the aim of this research is to quantify the influence of the window-to-wall ratio (WWR) input parameter in an UBEM implemented on the residential building stock in Escaldes-Engordany (Andorra). After gathering the WWR data from a significant sample of the building stock, heating simulations of the entire building stock were carried out using the extreme WWR values, minimum and maximum, in order to analyse the variation of the results. The results obtained show an average difference of 12% between the two heating consumption simulation results. Furthermore, it can also be seen that the differences are more significant for the single-family building typology, as well as for more recently constructed buildings.
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Acknowledgements
This research is part of the project TED-2021-132187B-I00 funded by MCIN/AEI/10.13039/501100011033 and the European Union NextGenerationEU/PRTR, and has also been funded by the predoctoral fellowship (ATC021-AND-2018/2019, 2019/2020) from the Government of Andorra granted to Patricia Borges.
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Borges, P., Pages-Ramon, A., Travesset-Baro, O. (2024). Quantifying Input Parameters Influence in UBEM Simulation Results: The Window-to-Wall Ratio Case. In: Littlewood, J.R., Jain, L., Howlett, R.J. (eds) Sustainability in Energy and Buildings 2023. Smart Innovation, Systems and Technologies, vol 378. Springer, Singapore. https://doi.org/10.1007/978-981-99-8501-2_58
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