Conclusions

This study described how to successfully create a detailed urban building energy model (UBEM) which is implemented with Functional Mockup Interface (FMI). UBE-FMI demonstrated that its FMI implementation enables extensive control over model components during simulation with the possibility to manipulate and exchange data at each time step. The result is the sub-hourly estimation of energy demand for every building on the basis of spatially and temporally varying climate conditions. The functionality of UBEM is enhanced through the incorporation of dynamically alterable parameters in the building models due to FMI. This novelty is highlighted in the scenario and can extend the applicability of UBEM to the operational management in public utilities, for instance, by informing about the impact of demand response strategies. Moreover, the dynamic modification of parameters in the building models, down to individual surfaces and components, can contribute to urban microclimate and heat island analysis by allowing detailed surveillance and control of heat exchange between the immediate environment and the building envelope. The scenario results suggest that slight changes in the indoor target temperatures of buildings, which were triggered by demand responses during peak hours, result in considerable energy savings. The long-term prospects of UBE-FMI lies in the planning and optimisation of energy saving and generation measures. In the development of UBE-FMI, it is shown how to automate the generation of three-dimensional building geometries from large-scale LiDAR data and achieve building models with higher Level-of-Detail than previous UBEMs. The comparison with reference studies suggests that the exemplary buildings in TABULA building typology are appropriate as templates for construction and building systems in an UBEM. The accompanying 3D visualization of the environmental impact of urban areas could foster a better environmental awareness of the wider public, as Google's Environmental Insight Explorer [22] or electricityMap [64] already demonstrate.
UBE-FMI has the potential to import data from behaviour-based load profiles [47] of occupants via FMI to better approximate their influence on energy demand. The involvement of municipalities and their public utilities could considerably further the development of UBEM by the provision of more localized data on buildings and actually metered energy data for validation.