A cooling demand estimator for housing communities in a warming world

Global warming has led to higher ambient temperatures in traditionally cold regions in Europe such as the UK. While implementing strategies in residential dwellings to meet the rising demand for cooling during hot summers is thus of interest, an accurate estimation of such demand is however a prerequisite for developing or implementing upgrades to cooling infrastructure. To contribute to this effort, this paper presents an estimation tool to quantify the cooling demand of a housing community. The tool was developed with the open-source software OpenModelica and was used to model diverse heat transfer phenomena in house envelope components, individual houses, and groups of houses. It uses multiple levels of design hierarchies and enables exploring different heat mitigation strategies. The tool was employed to estimate the potential future cooling demand of a UK housing community. The results highlight that houses of the same design may exhibit substantial variations in demand based on their location and orientation within the community. For instance, the annual demand of the houses ranges from 4505.8 kWh to 5873.4 kWh for the years under study if a cooling setpoint temperature of 21°C is adopted. By increasing this setpoint by 1.5°C, the community's annual demand could be reduced by ∼20 MWh. Furthermore, incorporation of mitigation strategies reduced both the overall and peak demands for the individual houses and the community as a whole while also decreasing the disparity in demand across households. By having access to the estimation tool, shared alongside the paper, interested users may be boosted to conduct ad-hoc assessments to understand cooling demand variations within any housing community of interest. • A model to quantify the cooling demand of housing communities due to climate change. • Provision to assess different active and passive cooling strategies. • Location and orientation of houses in the community affect their cooling demand. • Demand datasets to design cooling networks can be generated with the model. • Open-sourcing the Modelica model to enable customisation for different communities.