The UK has committed to net zero carbon emissions by 2050. Reducing emissions originating from the built environment, and particularly from the domestic sector, plays a significant role in meeting this target. Buildings —and the energy infrastructure providing them— have long asset lives. Therefore, understanding the nature of their long-term energy demand is key for ensuring that any solution or strategy adopted now continues to perform effectively in the future and for preventing assets becoming stranded. Current decision support methods used to manage future energy demands address this problem mainly by analysing economically or technically favourable paths to meet predicted demands. However, predictions based on past trends are not effective when the future does not unfold linearly. In contrast, explorative scenario analysis can help to identify a range of distinct and plausible paths that the UK residential energy demand could take in the future. This allows a fuller range of potential future uncertainties faced by decision-makers to be systematically considered, maximising the chances that the decisions taken now continue to deliver their benefits regardless of the future. Effective scenario analysis requires relevant, accurate and representative data. Indeed, it is possible to project coarse level information, or aggregated data, into future scenarios to broadly characterise them. However, more specific, direct and quantitative insights about that range of identified paths could improve the usefulness of scenario-based approaches. The use of finer grain information, or disaggregated data, in scenarios could deliver such insights as well as information of the likely behaviour of distinct groups of agents. Still, this is something the futures literature lacks. This research presents a simple tool —which comprises a mathematical framework and the method to use it— developed to project disaggregated household energy demand data into future scenarios in order to explore how such data can inform decisions regarding energy demand and supply. The performance of the tool was evaluated by projecting electricity and gas demand data into the scenarios from BRE’s toolkit Designing Resilient Cities (DRC). A method was developed to supplement DRC’s scenarios with needed indicators conveying detailed information about households and the way they use energy. The data evolutions found with those projections can be used to improve planning the UK household energy demand, for which examples are given. Furthermore, the scope of the tool, of the method to supplement scenarios and of the new indicators are not constrained to projecting household energy demand into future scenarios: the tool is capable of projecting any kind of disaggregated data which include sufficient metadata into any scenarios which meet certain conditions (have a typical architecture, are not too disruptive and characterise the variables aimed to be projected); any scenario with a typical architecture (a general narrative plus the characteristics of a set of indicators) can be adapted with the method developed here; and the adaptation of the DRC scenarios can be seamlessly used with the original toolkit.