Funded by: ESRC
University of Sheffield
Partners and stakeholders:
UK Collaborative Centre for Housing Evidence (CaCHE); Cambridge Architectural Research Ltd (CAR)
This research project focuses on the analysis of strategies to decarbonise the UK housing stock. This includes the further study of thermal flow and energy demands, as well as the integration of occupant behaviour to represent indoor interaction and processes of household decision-making. One of the major outcomes of this research has been the development of an open-source platform, in which academics and stakeholders might generate a set of typologies, look at the potential impact of such changes, and predict the likely adoption of strategies and technologies in response to underlying (social, financial, regulatory and educational) stimuli.
Numerous housing stock energy modelling (HSEM) platforms have been developed and deployed over the past 40 years, to better understand the effectiveness of strategies to improve the energy performance of the UK housing stock. These HSEMs have in common that they lack modularity and utilise poorly documented and physically simplified models that do not permit indoor temperature and thus comfort levels to be predicted. This latter means that the repartition of energy (lower bills) and comfort (higher temperatures) co-benefits from decarbonisation investments cannot be faithfully represented; potentially over estimating over-estimating potential carbon reductions arising from decarbonisation policy. Also, no HSEM is capable of predicting the likely impact of particular policy measures on household decisions to invest in decarbonisation technologies (e.g. insulation, lighting, heating and how water system) or to change their practices (e.g. heating patterns, switching off lights and hot water usage), depending on their demographic composition or their housing typology.
The EnHub platform has been developed in direct response to these observations. It is an open source and modular platform that rigorously and dynamically simulates a housing stock, based on state-of-the-art simulation techniques, as well as on scalable and cloud-ready computational workflows. Using open source datasets, primarily the English Housing Survey (EHS) and the Census, models of core housing typologies are prepared, accounting for location, built form and energy system. These core typologies are then enriched, to represent the distribution of parameters relating to building construction, occupancy and energy system that impact on energy use and carbon emissions. In its current form, this platform allows to rigorously model both energy demand and indoor comfort, and in so doing to also understand the impacts of trade-offs that households make between energy bills and indoor comfort and health. To sum, this research project contributes to systematically perform parametric evaluation (e.g. sensitivity analysis, evaluation of scenarios), and to integrate more sophisticated models of occupancy and usage (e.g. agent based model of household interactions and diffusion of technology). Such a contribution is meant to be a step-change in the evidential rigour supporting future housing energy demand improvements, and consequential carbon intensity reductions at national level.
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