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Project title

Exploring realistic pathways to the decarbonization of buildings in the urban context: a case study of Dublin city

Primary Funding Agency

Science Foundation Ireland

Co-Funding Organisation(s)

Lead Organisation

Trinity College Dublin

Lead Applicant

Not listed

Project Abstract

This application aims to develop machine learning (ML) approaches to tackle the challenges in urban building retrofitting for improving energy efficiency, faced by local authorities, policymakers, urban planning offices, and building owners. Specifically, to support sustainable urban renewal, we propose to explore ML-boosted approaches to tackle the challenges from three axes of sustainability: economic, social and engineering aspects. The ML-boosted approaches can help explore hidden socio-economic barriers to building retrofitting, mitigate the estimation error of building energy models due to incomplete/inaccurate data, and develop cost-efficient urban building retrofit design and planning solutions. As such, these solutions will have an impact on reducing carbon emissions and supporting the sustainable transition to carbon-neutral societies. By the end of the Seed phase, with the socio-economic analytic results, we seek to reshape our understanding of energy poverty and develop impactful strategies that can influence local and regional energy use and planning. By exploring approaches to support generative design and robust estimation, we aim to enable rapid trial and testing in the planning workflow of building retrofitting, and thereby help urban planners, building owners and designers identify viable and more cost-efficient solutions to improve building efficiency at an early stage.

Grant Approved

�254,949.00

Research Hub

Climate Change

Research Theme

3. Climate Solutions, Transition Management and Opportunities

Start Date

01/01/2023

Initial Projected Completion Date

30/06/2024