This research explores how climate change will affect building energy use across the UK by analysing both a conventional reference building design and a net-zero energy (NZEBs) alternative to assess how each would perform under future weather conditions. Using climate projections from databases like Prometheus and Meteonorm, along with simulation tools like EnergyPlus and Freds4Buildings, the study evaluates the energy performance, costs, and GHG emissions of a case study building under current weather conditions, with 2030, 2050, and 2080 forecasts in three different UK locations: Exeter, Manchester, and Aberdeen. Results indicate that heating demand will decrease consistently over time across all locations by as much as 21% by 2080 while cooling demand will rise sharply. NZEBs proved more resilient to these changes, using less energy and producing fewer GHG emissions than conventional buildings, with 89% reductions in emissions even with increased cooling needs. Accounting for future weather helps both understand the risks of conventional design, with a number of scenarios experiencing overheating in 2080 and ensure NZEBs can meet their goals during their entire lifespan despite the increases in energy needs. The study highlights both the impact of accounting for future weather forecasts during design and the increasing relevance of net-zero energy designs in mitigating the effects of climate change while offering practical insights for architects, policymakers, and energy planners, showing why future weather patterns need to be considered in sustainable building design to ensure buildings will achieve their carbon targets throughout their life.

The growing number of renewable energy communities (RECs) exemplifies the potential of citizen-driven actions towards a more sustainable future. However, obtaining hourly measured consumption data from REC members remains challenging, hindering accurate feasibility studies for the development of communities. This study examines the impact of estimating hourly consumption from aggregated data on REC analysis results. A case study with real consumption data from diverse users, representative of a typical community in Tuscany, Italy, was analysed to investigate various influencing factors. Multiple scenarios were simulated: two open-source tools estimated energy production from the community’s PV plants, and two REC configurations were considered—one with consumers and prosumers and another with consumers and a producer (with the same total installed power). Additionally, three locations were evaluated to consider the importance of geographical positioning. The study revealed that the impact of consumption data aggregation is more significant in scenarios with low energy sharing, such as the scenario where prosumers were replaced with a producer. Geographical positioning showed no major discrepancies in energy and economic outcomes, implying that using estimated hourly consumption data from aggregated data has a relevant impact regardless of location. Furthermore, different weather files did not affect the impact of aggregated consumption data.