Characterizing and structuring open datasets for assessment of residential building energy use on the neighborhood and urban scale

• Leverages open datasets to develop a scalable urban building energy model. • Achieves robust calibration against multi-year hourly electricity consumption profiles. • Underscores the need and utility of versioned, authoritative building data to improve UBEM accuracy. • Demonstrates how georeferencing reveals spatial and temporal patterns in building energy use. This study investigates the potential of utilizing and integrating open datasets to assess and visualize energy use in the residential building stock by employing models aligned with national calculation methods, statistics and aggregated data from smart metering platforms. A case study of a Norwegian municipality is employed to illustrate these concepts. The study also examines the possibilities and limitations of using open data to reconstruct building geometry and derive relevant information to generate a new enriched dataset consistent with statistics. Enabling the use of open data and interoperable modeling frameworks is crucial for developing tools that cater to various stakeholders and evolving use cases. The findings demonstrate that the implemented residential building energy model, validated against multiple years of hourly aggregated electricity consumption, performs well on daily and monthly timescales, consistently meeting validation thresholds specified for CV(RMSE) and NMBE. The model’s performance on an hourly basis—critical for assessing strategies to reduce peak electricity loads during the coldest hours—could be improved with more advanced approaches. Additionally, if authorities regularly release up-to-date building footprints, these can be effectively combined with other open datasets to create more accurate inputs for archetypes or georeferenced building simulations, but future releases should be versioned to ensure their applicability. Georeferencing enables the visualization of results in thematic maps, illustrating the impact of future developments at the neighborhood scale, while also facilitating comparisons between building-specific outcomes and area-wide averages or archetypes, potentially empowering individual building owners with actionable insights.