## 4 Discussion
### AI and Cities  
The European Commission's recent proposal for a regulation, known as the Artificial Intelligence Act, holds significant implications for the construction and urban development sectors. This act is a testament to the growing recognition of AI's transformative potential,especially within the construction ecosystem. It seeks to strike a harmonious balance in the deployment of AI systems, particularly for professionals in the construction sector who are increasingly relying on data-driven systems within the European Union. Key areas of concern for the AEC sector encompass the establishment of harmonised rules for AI systems, the delineation of specific requirements for high-risk AI systems, and the introduction of supportive measures to foster innovation [63]. In the broader urban context, cities are emerging as focal points in AI discourse. As underscored by the United Nations Habitat in their report titled \"AI and Cities\" [64], cities are uniquely positioned to harness the capabilities of AI to address a myriad of socio-economic and ecological challenges. As urban centres grapple with multifaceted issues ranging from resource constraints to governance complexities and mounting environmental threats, the infusion of AI-driven innovations is becoming indispensable. However, to truly capitalise on the transformative potential of AI, a concerted effort is required from various stakeholders to create an ecosystem that promotes sustainable and inclusive development. This necessitates a judicious balance between leveraging the opportunities presented by AI and mitigating the associated risks. While numerous countries have proactively rolled out national AI guidelines [65], local governments, urban planners, and policymakers are navigating the intricate maze of developing, implementing, and evaluating regulatory frameworks tailored to their unique urban contexts. The rapid pace of AI advancements has opened up a plethora of potential applications in urban settings. The United Nations, in its deliberations, has spotlighted several sectors ripe for AI integration, including \"energy, mobility, public safety, water and waste management, healthcare, urban planning, and city governance\" [64]. One sector in which the impact of AI is particularly pronounced is energy. Data-driven systems, underpinned by AI, are poised to revolutionise energy management, propelling cities towards a more sustainable, low-carbon future. These systems are adept at forecasting the energy supply from various sources, facilitating predictive maintenance, and optimising energy distribution [64]. As urban landscapes increasingly pivot towards renewable energy sources, the role of data-driven models in forecasting, planning, and optimising energy consumption and distribution becomes paramount. These models can harness a diverse array of data to generate forecasts, offering insights into emissions and consumption patterns in intricate urban matrices. However, for several years, climate datasets tailored to building performance simulations have been accessible, primarily due to the inception of an effective data format known as the typical meteorological year (TMY) [66]. This was further bolstered by the global availability of data in this format. Beyond merely expanding the global reach of these datasets, recent research endeavours have delved into techniques used to simulate local microclimatic events within urban landscapes, such as the urban heat island effect [67]. In a research study focused on London, [PERSON] et al. amalgamated temperature profiles obtained locally with an Urban Building Performance Simulation (U-BPS). This integration aimed to elucidate the influence exerted by the urban heat island effect on not only the energy consumption of buildings but also on the well-being of the residents within these structures [68].