Hey, J., Siebers, P-O., Nathanail, P. et al. (2 more authors) (2023) Surrogate optimization of energy retrofits in domestic building stocks using household carbon valuations. Journal of Building Performance Simulation, 16 (1). pp. 16-37. ISSN 1940-1507
Abstract
Modelling energy retrofit adoption in domestic urban building stocks is vital for policymakers aiming to reduce emissions. The use of surrogate models to evaluate building performance combined with optimization procedures can optimize small building stocks but are insufficient at the urban scale. Recent methods train neural networks using samples of near-optimal solutions further decreasing the computational cost of optimization. However, these models do not make definitive predictions of decision makers with given environmental preferences. To rectify this, we extend the method by assigning a carbon valuation to households to derive their optimal retrofit solutions. By including the carbon valuation when training the predictive model, we can analyze the impact of households' changing attitudes to emissions. To demonstrate this method we construct an agent-based model of Nottingham, finding that simulated government campaigns to boost environmentalism improve both the number of retrofits performed and the mean emissions reduction of each installation.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Energy performance retrofits; building stock analysis; surrogate modelling; machine learning; optimization |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > School of Architecture (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 02 Nov 2022 16:05 |
Last Modified: | 25 Sep 2024 12:04 |
Status: | Published |
Publisher: | Taylor and Francis Group |
Refereed: | Yes |
Identification Number: | 10.1080/19401493.2022.2106309 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:192837 |
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Licence: CC-BY-NC-ND 4.0