Project title

Socio-technical community energy model

Principal investigator

Dr David Jenkins, Heriot-Watt University

Project team

• Professor Gordon MacKerron, University of Sussex
• Dr Andrew Peacock, Heriot-Watt University
• Dr Peter McCallum,  Heriot-Watt University
• Dr Paraskevi Vatougiou, Heriot-Watt University
• Claire Copeland, University of Sussex

Project summary

Traditional, theoretical building modelling, particularly that used in stock models for low-carbon building policy, provides challenges for representing specific household types at scale. Whether using national, steady-state stock models (such as the Cambridge Housing Model) or dynamic urban energy models (operating at smaller spatial scales), the ability to represent different demographics and household characteristics is limited. More often, such models estimate the aggregated energy consumption of a large number of homes based on average/typical household definitions, rather than characterising the significant diversity across a community.

For some applications, this approach is defendable, where an approximate representation of energy consumption or carbon emissions (at very low temporal resolution) is required. However, when wanting to profile energy demand at a higher resolution, the nuances of household composition and consumption patterns become more important. Whilst true for establishing a current modelled baseline, this becomes even more important when projecting the potential impact of future scenarios on specific communities. Scenarios, such as National Grid Future Energy Scenarios, are multi-faceted and will impact many different aspects of energy use (and provision) in a home, but they are not generally designed for granular application. For example, a future vision that requires high capital cost expenditure on new heating technologies is unlikely to have the same impact on two residential areas with different deprivation indices – regardless of whether the physical building stock is similar or not. This means that for energy suppliers, local authorities, technology providers and other actors within energy systems, it is difficult to understand what a top-down, generic (national) future scenario will mean for a specific community, when attempting to make provision for this over the coming decades.

With this conundrum in mind, this project will take a novel community energy demand model, already prototyped through CESI, and align it to household socio-demographic indicators. Narratives from future energy scenarios will be translated through expert elicitation and workshop exercises (as described below) to produce meaningful urban energy model inputs. The model, being informed by local GIS data, Energy Performance Certificate surveys, and energy demand data (as a proxy for occupancy and behaviour), can be tailored on a specific residential region (up to 1000s of dwellings). The dynamic, high-resolution nature of the model (powered by an EnergyPlus simulation engine) allows for important physical properties of heat and energy to be modelled transiently (e.g. diurnally), whilst also providing an opportunity to account for activity variations linked to specific occupants. Finally, the ability to run this efficiently at scale allows for the generation of aggregated energy consumption metrics for large residential regions.

The versatility of this model will enable some of the future scenario mapping work of CESI to be incorporated directly into estimations of community energy demand - in a way not currently possible in CESI or similar projects. The impact of future energy scenarios on specific communities, with specific metrics (income, household composition, working practices, technologies etc), will be estimated - indicating how different futures have different (potentially unequal) impacts on communities.

Workshop outputs

Report here: CESI Flex Fund Workshop Outputs David Jenkins

Final report

Report here: CESI Flex Fund Final Report Socio Technical Community Energy Modelling- David Jenkins‌

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