RCUK Innovation Fellowship in UK Housing Stock Decarbonisation
Lead Research Organisation:
University of Sheffield
Department Name: Architectural Studies
Abstract
Several models have been developed in the UK to predict the energy and associated carbon emissions of the UK housing stock. These have been used to predict the reductions in energy use and carbon emissions arising from alternative renovation options (e.g. insulating walls and roofs, draught stripping, fitting more efficient heating systems), based on different scenarios in terms of the adoption of these renovation options. None of the existing models is able to predict the adoption of renovation options in response to specific policy measures that are designed to stimulate them. Examples of these include building regulations, financial subsidies, taxation relief and educational campaigns. To address this shortfall, we will develop a new prototype social simulation platform that will model the likelihood that households will invest in energy reducing renovation options and / or change their energy using behaviours, in response to policy measures. This platform will be coupled with a new dynamic housing stock energy model, enabling us to predict the nationwide impacts of households' decarbonisation decisions.
This combined physical and social simulation platform will be deployed in the UK to evaluate the effectiveness of alternative housing decarbonisation policy measures. To enable us to design and calibrate the social simulation platform, we will also design and conduct a pilot UK homeowners' social survey. This will be complemented with existing secondary datasets, to characterise household (to complement existing housing) archetypes and their attributes.
This combined physical and social simulation platform will be deployed in the UK to evaluate the effectiveness of alternative housing decarbonisation policy measures. To enable us to design and calibrate the social simulation platform, we will also design and conduct a pilot UK homeowners' social survey. This will be complemented with existing secondary datasets, to characterise household (to complement existing housing) archetypes and their attributes.
Planned Impact
The beneficiaries of the proposed research are city and national policymakers that are concerned with reducing greenhouse gas emissions and improving housing conditions [policy impacts], the construction industry [economic impacts] and individual homeowners [quality of life impacts].
This research will provide policymakers with unique insights into the effectiveness of policy measures in reducing greenhouse gas emissions and / or in improving living conditions; enabling them to predict for the first time the likely uptake and corresponding energy (and in the future the comfort and health) impacts (and where these impacts are realised, both geographically and demographically) of specific renovation options (e.g. insulation and draught stripping, heating and hot water system) in response to the policy measures designed to stimulate them; enabling them to tune their policy measures.
It is expected that close collaboration with members of the CaCHE network, augmented with the engagement of our partner Cambridge Architectural Research Ltd, will be instrumental in successfully engaging with policy makers. In this we will strive to inform the content of relevant government policy instruments. Furthermore, by making the research outcomes (the modelling platform and survey designs) openly available, the research has the future potential to achieve multinational policy impact.
The construction industry, as well as the power-generation industries, will also benefit from a more thorough and targeted understanding of the business opportunities that exist for the construction of new and renovation of existing low carbon housing. Finally, individual homeowners will benefit from improved living conditions, with corresponding impacts on their health, comfort and wellbeing; particularly those that are currently living in energy poverty.
This research will provide policymakers with unique insights into the effectiveness of policy measures in reducing greenhouse gas emissions and / or in improving living conditions; enabling them to predict for the first time the likely uptake and corresponding energy (and in the future the comfort and health) impacts (and where these impacts are realised, both geographically and demographically) of specific renovation options (e.g. insulation and draught stripping, heating and hot water system) in response to the policy measures designed to stimulate them; enabling them to tune their policy measures.
It is expected that close collaboration with members of the CaCHE network, augmented with the engagement of our partner Cambridge Architectural Research Ltd, will be instrumental in successfully engaging with policy makers. In this we will strive to inform the content of relevant government policy instruments. Furthermore, by making the research outcomes (the modelling platform and survey designs) openly available, the research has the future potential to achieve multinational policy impact.
The construction industry, as well as the power-generation industries, will also benefit from a more thorough and targeted understanding of the business opportunities that exist for the construction of new and renovation of existing low carbon housing. Finally, individual homeowners will benefit from improved living conditions, with corresponding impacts on their health, comfort and wellbeing; particularly those that are currently living in energy poverty.
People |
ORCID iD |
Gustavo Sousa (Principal Investigator / Fellow) |
Publications
Sousa G
(2020)
Enhanced EnHub: dynamic simulation of housing stock energy systems
in Journal of Building Performance Simulation
Sousa G
(2018)
An open-source simulation platform to support the formulation of housing stock decarbonisation strategies
in Energy and Buildings
Title | EHS in-depth processing |
Description | The purpose of this dataset/model is to is threefold: to automate the conversion of raw data survey into an appropriate format to be employed by housing stock energy models; to combine multiple EHS versions to conform the basis of a machine learning training dataset with which to support a predictive model; to increase the enhance the collection and mining of household-related information to be later linked with other similar datasets. |
Type Of Material | Database/Collection of data |
Year Produced | 2019 |
Provided To Others? | No |
Impact | The dataset has helped developed more sophisticated models to evaluate the housing stock, and it has influenced the development of further research regarding housing circumstances and processes of decision-making. |
Title | Household profiles |
Description | The purpose of this model is to conform a variety of household compositions, based on socio-demographics and dwelling oriented features. These compositions will be employed to improve the study of indoor energy performance, by enabling the ability to represent individual and household interaction. The new database, is initially created by combining the English Housing Survey and the United Kingdom Time Use Survey, which respectively require license agreements. |
Type Of Material | Database/Collection of data |
Year Produced | 2018 |
Provided To Others? | No |
Impact | Such an enhancement will in turn support the development of EnHub, which in its next stage will implement agent-modelling approach to study diffusion of technology, as well as rebound effects. Simultaneously, such additions will also be directly benefited by a dedicated social survey, yet to-be-undertaken, which will provide explicit information on dwelling and household circumstances. On the one hand, these profiles help to deploy synthetic populations and to study differences on energy performance based on energy intensity and usage; on the other, these profiles help to scrutinise processes of decision-making that are typically found in practice, which depend on external factors (to the energy performance). |
Title | Housing Stock Energy Hub (EnHub) |
Description | EnHub is platform for the dynamic simulation of national housing stocks. This platform extracts survey data, generates volumetric archetypes, quantifies uncertainties and explores the potential impacts of policies and strategies to decarbonise the stock, initially the UK. The platform couples R (the statistical software) and a building energy simulation software, called Energy Plus. It can be run using an IDE, directly from a terminal or via scripts, so that the processes can be performed in different machines, including HPC facilities. This is to increase its usability, accessibility and avoid dependencies on hardware features. The platform itself is shared under an open-source license, however each of the data sources is licensed under diverse and relatively specific agreements. It is the user's responsibility to agree on these terms to use our platform. |
Type Of Material | Computer model/algorithm |
Year Produced | 2019 |
Provided To Others? | Yes |
Impact | To date, the release of the platform has received attention from academic colleges; however, the dependency with external data sources, which requires multiple license agreements has in turn undermined the utility of EnHub. This is an issue we are currently addressing. |
URL | http://github.com/EnHub-UK |
Title | Dynamic simulation of housing stock energy systems |
Description | In the UK, heating systems are the most prominent contributor to residential energy demand, with about 80% of the share. The representation of heating systems has thus been at the core of all UK-focussed Housing Stock Energy Models (HSEMs). However, in the majority of these HSEMs, heating demand has been estimated using an approximate annual or monthly energy balance, with correspondingly approximate representations of heating practices and systems (incl. energy conversion, distribution and spatiotemporal control systems) and occupants' energy-related behaviours. Such assumptions have in turn undermined the ability to faithfully evaluate specific interventions impacting on space heating and domestic hot water use, and to provide the corresponding evidence base to support the formulation of robust policy interventions. This "dynamic simulation of housing stock energy systems" has been designed and developed to address this. The new software rigorously represents space heating and hot-water components (i.e. heaters, boilers, pumps, radiators, end-point registers, thermostats, taps), and is able to estimate the potential impact of large-scale interventions, using this more refined representation. |
Type Of Technology | Software |
Year Produced | 2019 |
Open Source License? | Yes |
Impact | The new addition allows us to estimate alternative scenarios of heating and hot-water system substitution, which helps us to reveal the potential impact of large-scale interventions. This gives a more robust method to formulate policies related to heating improvements. |
Description | A Dynamic Platform For Uk Housing Stock Decarbonisation |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Public/other audiences |
Results and Impact | This blog post outlined the challenges we face decarbonising the UK housing stock, and provided an insight into the new open-source dynamic modelling platform (EnHub) that has been developed to solve this issue. It also mentioned some of the current work that is being made as well as some directions of future research. |
Year(s) Of Engagement Activity | 2020 |
URL | https://housingevidence.ac.uk/ukri-fellowship-a-dynamic-platform-for-uk-housing-stock-decarbonisatio... |
Description | webinar / Achieving net zero: decarbonising the UK housing stock |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | This webinar presented a review on the UK's commitment to cut down CO2 emissions in the residential sector, and highlighted how a combination of reductions in energy demand and the decarbonisation of heat provision and the wider power system is required to this target. In this talk, we also highlighted the major issues to be addressed for this transition to happen, and the suitability of enhanced stock energy models to support the formulation of policies and strategies in the sector. The webinar was organised by the UK Collaborative Centre for Housing Evidence (CaCHE), the institution which my research is affiliated with; it was attended by academics and non-academics (incl. housing associations and practitioners). |
Year(s) Of Engagement Activity | 2020 |