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Data fusion to synthesise quantitative evidence, value and socio-economic factors: A framework and example of Dempster-Shafer theory

Orr, S; (2018) Data fusion to synthesise quantitative evidence, value and socio-economic factors: A framework and example of Dempster-Shafer theory. In: Broström, T and Nilsen, L and Carlsten, S, (eds.) Conference Report: The 3rd International Conference on Energy Efficiency in Historic Buildings. (pp. pp. 163-171). Uppsala University: Uppsala, Sweden. Green open access

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Abstract

This paper presents a framework and example of how fuzzy data fusion processes can support decision making for energy efficiency in historic buildings. Dempster-Shafer (DS) theory is a framework of reasoning that deals with uncertainty, allowing one to combine evidence from different sources. DS theory can handle conflicting information, with the aim to provide a representation of the appropriateness and uncertainty for each option. The theory starts with a set of possibilities: for example, a range of retrofit options or energy-use schemes. Each one is assigned a degree of belief depending on how many evidence inputs contains the proposition and the subjective probability. DS theory incorporates hard data, e.g. energy models and economic estimates, and opinion, e.g. disruption to activities and changes in aesthetics. It is proposed that DS Theory and hard-soft data fusion algorithms provide an approach that can incorporate value and socio-economic aspects into decision making.

Type: Proceedings paper
Title: Data fusion to synthesise quantitative evidence, value and socio-economic factors: A framework and example of Dempster-Shafer theory
Event: 3rd International Conference on Energy Efficiency in Historic Buildings
Location: Visby, Sweden
Dates: 26 September 2018 - 28 September 2018
ISBN-13: 978-91-519-0838-0
Open access status: An open access version is available from UCL Discovery
Publisher version: http://urn.kb.se/resolve?urn=urn%3Anbn%3Ase%3Auu%3...
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: decision making; data fusion; artificial intelligence; uncertainty; conflict resolution
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources
URI: https://discovery.ucl.ac.uk/id/eprint/10083125
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