Knowledge about the demand of renewable thermal energy is essential for the integrated planning of sustainable cities. Planners and decision-makers need high-quality and comprehensive data for rapid and decisive action. However, little to no corresponding information is available for the status quo or possible development scenarios. Thus, new methods for urban building energy modelling and simulation with high resolution and accuracy are needed. In this paper, we present the research we conducted on the thermal energy demand of a city district in Graz, Austria. We developed a novel bottom-up, white box, multi-tool workflow for performing large-scale and high-resolution modelling, simulation and analysis of urban buildings in different development scenarios. We calculated the demand for heating, cooling and hot water in full-year dynamic building simulations and assessed the results quantitatively and spatially. Our results in the Scenario 2050 show that despite massive densification of the building stock by 88%, the calculated heat energy demand rises only by 4%; while the cooling demand soars by 432%. All results are available as hourly mean values and annual totals and in easy-to-understand spatial map representations, thus supporting stakeholders to meet the net-zero CO₂-equivalent emission targets of Graz.

Efficient and accurate grey-box building models, including water-based heating and cooling systems, are crucial for simulating and optimizing the energy demand of building, neighborhood, and network scenarios. However, the numerical effort and the amount of input data required for existing models are still high, and the parameterization of these systems is very labor-intensive. This paper presents a grey-box model that addresses these limitations by requiring minimal input data and offering a highly efficient parameterization method. Using physical principles, the model was validated against a detailed physical building model and measurement data. Our results show that the grey-box model accurately predicts return temperatures (σ = 0.37 K, µ = 0.05 K) and room air temperatures (σ = 0.62 K, µ = 0.28 K). Compared to 8229 s for the detailed physical model, the model requires only 18 s for a one-year simulation. The model also shows robust behavior with alternative weather data and control strategies. The key contribution of this work is the development of a grey-box model that combines high accuracy and numerical efficiency with significantly reduced data and parameterization requirements, with possible applications in large-scale building simulations, demand-side management, short-term energy storage strategies, and model predictive control.

In this demonstration paper, we present our interactive virtual reality (VR) experience, which has been designed to facilitate interaction with energy-related information. This experience consists of two main modes: the world in miniature for large-scale and first-person for real-world scale visualizations. Additionally, we presented our approach to potential target groups in interviews. The results of these interviews can help developers for future implementation considering the requirements of each group.