Challenges and Lessons Learned in Applying Sensitivity Analysis to Building Stock Energy Models
Paper i proceeding, 2022

Uncertainty Analysis (UA) and Sensitivity Analysis (SA) offer essential tools to determine the limits of inference of a model and explore the factors which have the most effect on the model outputs. However, despite a well-established body of work applying UA and SA to models of individual buildings, a review of the literature relating to energy models for larger groups of buildings undertaken by Fennell et al. (2019) highlighted very limited application at larger scales. This contribution describes the efforts undertaken by a group of research teams in the context of IEA-EBC Annex 70 working with a diverse set of Building Stock Models (BSMs) to apply global sensitivity analysis methods and compare their results. Since BSMs are a class of model defined by their output and coverage rather than their structure and inputs, they represent a diverse set of modelling approaches. Key challenges for the application of SA are identified and explored, including the influence of model form, input data types and model outputs. This study combines results from 7 different modelling teams, each using different models across a range of urban areas to explore these challenges and begin the process of developing standardised workflows for SA of BSMs.

Författare

P. Fennell

University College London (UCL)

M. Van Hove

Universiteit Gent

Lia Weinberg

TEP Energy

George J. Bennett

University College London (UCL)

M. Delghust

Universiteit Gent

Sebastian Forthuber

Technische Universität Wien

Martin Jakob

TEP Energy

Erika Mata Las Heras

IVL Svenska Miljöinstitutet

Claudio Nägeli

Chalmers, Arkitektur och samhällsbyggnadsteknik, Byggnadsteknologi

J. L. Reyna

National Renewable Energy Laboratory

Giacomo Catenazzi

TEP Energy

Building Simulation Conference Proceedings

25222708 (ISSN)

2203-2210
9781775052029 (ISBN)

17th IBPSA Conference on Building Simulation, BS 2021
Bruges, Belgium,

Ämneskategorier

Annan data- och informationsvetenskap

Annan samhällsbyggnadsteknik

Sannolikhetsteori och statistik

DOI

10.26868/25222708.2021.30960

Mer information

Senast uppdaterat

2023-10-26