Modelling whole cities’ building energy in the electrification era

Buildings sit at the centre of this century’s energy and climate future, yet the energy life of entire building stocks remains largely invisible. Demand is scattered across cities, shaped by weather and human behaviour, and shielded from view by privacy rules that restrict access to household-level data. A new review in Nature Reviews Electrical Engineering argues that urban building energy modelling, or UBEM, is the tool that can make this hidden demand visible, and that the field now faces a decisive test from two directions at once.

UBEM simulates the energy behaviour of entire building stocks rather than single structures, bridging micro-level building physics and macro-level urban energy system optimization. Instead of modelling one house’s heating system or one office tower’s cooling load, the approach aggregates physics-based and data-driven representations of thousands of buildings into a city-scale picture of where energy is used, when it is used and how that pattern might change. According to the review, that capability is being tested by climate change, which is rewriting the thermal conditions buildings must withstand, and by electrification, which is recasting buildings as active players in power grids rather than passive consumers.

The scale of the challenge is considerable. Buildings account for a large share of global energy use and emissions, but urban-scale demand stays difficult to quantify because it is dispersed, heterogeneous, behaviour-driven and restricted by data privacy. The review describes this as a data paradox: the information needed to simulate city energy systems is precisely the information that privacy constraints and fragmented ownership make hardest to collect. How the field responds, the authors argue, will determine whether UBEM remains a specialist simulation tool or becomes civic infrastructure for the energy transition.

That transition is changing what models must capture. Under warming scenarios, urban cooling and heating degree-days diverge between 1.5 °C and 2.0 °C of warming, and cooling degree-days along with heatwave frequency rise across the United States under future climate scenarios. These shifts alter the baseline conditions every simulation must assume. A model calibrated on historical weather can misrepresent future demand for cooling, peak electricity loads and the thermal stress buildings experience during heatwaves, which is why the review identifies climate representation as one of the gaps now exposed in high-resolution multiload modelling.

Electrification adds a second pressure. As heating, cooking and transport shift onto the grid, buildings evolve from passive users into proactive grid service providers, able to shift demand, store energy and respond to price signals. The review notes that this exposes gaps in high-resolution multiload modelling, meaning models must track electricity, heating and cooling loads together at fine spatial and temporal resolution to anticipate grid stress. A city-scale model that cannot resolve when and where electrified demand peaks will miss the moments when the power system is most constrained.

The methodological response described in the review extends UBEM across the building–mobility–grid nexus, couples it with climate modelling for adaptation, and embeds artificial intelligence across the modelling pipeline to ease the data paradox. Four mainstream UBEM approaches and their workflows are catalogued in the review, reflecting a field that has grown from a single technique into a family of methods with different trade-offs between physical detail, data requirements and computational cost. Coupling UBEM with climate models allows adaptation planning to proceed alongside energy planning, while AI techniques can fill gaps where measured data is scarce.

These capabilities, the authors argue, let UBEM link energy performance with public health and guide inclusive electrification and urban regeneration, shifting it from an energy calculator towards a cross-domain decision hub. Anticipating grid stress before it happens, protecting health during heatwaves and steering investment towards the communities that need it most are the functions the review envisages. A model that can identify which neighbourhoods face the highest cooling loads during heatwaves, for example, can inform both grid planning and public health protection at the same time.

The review also sets out what the next decade demands of the field. First, it should break the data–validation deadlock, the persistent problem that models cannot be trusted without measured data and data cannot be shared without privacy guarantees. Second, it should integrate UBEM into multisectoral urban systems, so that building energy simulation connects with mobility, grid and climate planning rather than operating in isolation. Third, it should align its metrics with sustainable development goals, giving city governments and investors a common yardstick for comparing energy, emissions and equity outcomes.

The authors, led by Rui Jing and Meng Wang with colleagues including Chenghao Wang, Haoran Zhang, Yingru Zhao, Jianzhong Wu, Nilay Shah and Jinyue Yan, frame the stakes in civic terms. Whether UBEM becomes a specialist tool or civic infrastructure, they write, depends on how the field responds to climate change and electrification now. The review, published on 29 October 2026, examines how the field is rising to that challenge and what the coming decade requires, positioning urban building energy modelling as a potential foundation for the energy transition in cities worldwide.

Subject of Research: Urban building energy modelling for simulating city-scale building energy demand under climate change and electrification

Article Title: Urban building energy modelling in the electrification era

Article References: Jing, R., Wang, M., Wang, C., Zhang, H., Zhao, Y., Wu, J., Shah, N., & Yan, J. (2026). Urban building energy modelling in the electrification era. Nature Reviews Electrical Engineering. https://doi.org/10.1038/s44287-026-00348-w

Image Credits: AI Generated

DOI: 10.1038/s44287-026-00348-w

Keywords: urban building energy modelling, UBEM, electrification, climate change, building energy demand, power grids, heatwaves, data privacy, artificial intelligence, urban energy systems, grid-interactive buildings, energy transition