## 1 Introduction  
Since the dawn of the industrial revolution, urban environments have emerged as significant influencers of energy consumption patterns. Today, as urbanisation continues its relentless march, over half of the global population resides in cities. These urban hubs, while being vibrant centres of culture and commerce, are also responsible for a staggering 75% of global carbon emissions [1]. This urban-centric carbon footprint underscores cities' dual role: they are both major contributors to the climate challenge and, simultaneously, pivotal players in its solution. A testament to this is the commitment of over 100 cities worldwide to achieve net-zero carbon emissions by 2050 [2]. Within this urban landscape, buildings stand out, playing an indispensable role in the carbon equation. Yet, while the importance of buildings in this narrative is clear, the roadmap to achieving substantial carbon emission reductions remains shrouded in complexity. The 21 st century presents a defining challenge for the built environment: constructing energy-efficient urban spaces while retrofitting existing ones to meet modern standards. Addressing this challenge demands a granular understanding of how buildings consume energy and the strategies required to minimise this consumption. Building designers,policymakers, administrators, and even tenants, must come together, pooling their expertise to devise and implement impactful, cost-efficient measures [3]. Building performance simulations (BPSs) have long been the bedrock of decision-making in building design. As shown in Figure 1, BPS plays a critical role from the initial planning stage through to occupancy, with its influence peaking during the design phase. As legislative frameworks evolve--pushing for greener, more efficient buildings--BPS has adapted, integrating richer, more reliable data streams into the design process. The digital age has ushered in an era of unprecedented data availability, paving the way for the integration of machine learning and other data-driven tools into BPS. These advancements promise not only enhanced simulation and modelling methods but also their seamless integration right from the earliest design stages. In this scenario, the recent revision of the Energy Performance of Buildings Directive (EPBD) [4] echoes this sentiment, emphasising the integration of data-informed and performance-driven tools into BPSs. Yet, the path is not without its hurdles. The BPS realm offers a vast array of modelling methods, creating a maze that often leaves stakeholders, from researchers to policymakers, perplexed [5]. On the other hand, the data, the lifeblood of these models, are often fragmented, unstructured, or locked behind gates of inaccessibility. The early design stage of a building is a period of immense potential. It is during these phases that critical design parameters are set, influencing over 40% of a building's energy-saving potential [6]. To harness this potential, there is a pressing need to optimise these parameters from the earliest stages. While the allure of data-driven models, with their promise of rapid and reasonably accurate energy consumption estimates, is undeniable, traditional BPS methods cannot be overtaken with ease. Even as data-driven BPSs evolve, challenges persist, from computational intensity to potential overestimations of the energy savings [7], from underlying biases to unclear data processing. However, recent research [8; 9] shines a spotlight on the potential of data-driven methods, with techniques like Artificial Neural Networks (ANNs) [10; 11], Support Vector Machine (SVM) [12; 13], and Decision Tree-based models (DT) [14] gaining traction in building energy modelling [15]. While several reviews have analysed specific tools [8; 16], simulation techniques and algorithms, and related indices and evaluation tools [7; 17; 18], this study pivots to explore the current state of the art concerning the integration of data-driven tools in the architecture, engineering, and construction (AEC) sector.