## 4 Discussion
### AI and Cities
Concurrently, there is a burgeoning interest in forecasting local wind trajectories [69] and in aligning current TMYs with climate change projections from the Intergovernmental Panel on Climate Change (IPCC) [70]. These are pivotal research areas with direct ramifications for U-BPSs. In many instances, researchers have amalgamated the export/import functionalities of existing platforms, such as Geographic Information Systems (GIS) and Building Information Modelling (BIM), with bespoke scripts. This approach facilitates the creation of a thermal model, oversees the simulations, and presents the findings through tools like spreadsheets or GIS applications [68; 71; 72; 73; 74]. Some teams have further refined and automated these simulation workflows, incorporating additional urban performance indicators to make U-BPS more accessible to urban planners and designers. The efficacy of U-BPS models in informing design choices or policy decisions hinges significantly on the accuracy of the simulation outcomes. Considering the potential discrepancies between individual BPS predictions and actual measurements, due to variables like infiltration rates and occupant behaviours, one might question the U-BPS's capacity to authentically forecast energy consumption across numerous buildings. Yet, when juxtaposing aggregated yearly measured data against simulated energy usage spanning multiple structures, the individual model deviations appear to neutralise. This results in documented error margins of merely 7% to 21% for heating loads [73; 75; 76] and a range of 1% to 19% for the overall Energy Use Intensity (EUI) [77; 78; 79; 80]. The integration of AI and machine learning models into U-BPSs has garnered considerable attention in recent years. [PERSON] and [PERSON] (2019) delved into the amalgamation of physics-based building simulation methodologies and machine learning techniques, with a particular focus on transfer learning. Their exploration aimed to evaluate the effects of retrofit policies on urban structures [81]. This integrated methodology, dubbed Data-driven Urban Energy Simulation (DUE-S), showcased its potential in identifying the energy implications urban environments have on buildings undergoing retrofit processes. In a similar vein, [PERSON] et al. (2021) conducted an examination into the viability of establishing Positive Energy Districts (PEDs) within various urban typologies located in Wien [82]. Their work underscored the necessity of implementing extensive energy efficiency measures, promoting electrification, and harnessing renewable energy sources, as pivotal steps in the transformation of existing building stocks into PEDs. Moreover, the imperative of understanding the building stock at a larger scale, with a spotlight on building geometry, was accentuated by [PERSON] et al. (2022) [83]. Their research unveiled a novel methodology designed to automatically gauge the building dimensions derived from remote sensing data, employing unsupervised machine learning algorithms in the process. Concluding their series of studies, [PERSON] et al. (2022) stressed the crucial role of modelling in the adoption of energy retrofits within urban residential building stocks. They introduced an innovative concept in which carbon valuations are assigned to households, serving as a determinant for identifying optimal retrofit solutions [84]. Their approach combined surrogate models, optimisation procedures, and neural networks to evaluate building performance, offering insights into the potential of such models in informing policy decisions. These AI-driven models are equipped to provide granular insights into the energy demand within buildings [85], elucidate energy dynamics in urban microclimates [86], and categorise distinct energy demand patterns [87], thereby facilitating more informed decision-making.