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Surrogate optimization of energy retrofits in domestic building stocks using household carbon valuations

Hey, James; Siebers, Peer Olaf; Nathanail, Paul; Ozcan, Ender; Robinson, Darren


James Hey

Paul Nathanail

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Professor of Computer Science and Operational Research

Darren Robinson


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.


Hey, J., Siebers, P. O., Nathanail, P., Ozcan, E., & Robinson, D. (2022). Surrogate optimization of energy retrofits in domestic building stocks using household carbon valuations. Journal of Building Performance Simulation,

Journal Article Type Article
Acceptance Date Jul 18, 2022
Online Publication Date Sep 2, 2022
Publication Date Sep 2, 2022
Deposit Date Oct 1, 2022
Publicly Available Date Oct 3, 2022
Journal Journal of Building Performance Simulation
Print ISSN 1940-1493
Electronic ISSN 1940-1507
Publisher Routledge
Peer Reviewed Peer Reviewed
Keywords Computer Science Applications; Modeling and Simulation; Building and Construction; Architecture
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