## 1 Introduction  
### Background  
As of June 2024, 107 countries, accountable for about 82% of global greenhouse gas emissions, had adopted pledges to achieve net-zero emissions [1]. Decarbonization of the building sector is a crucial component in achieving contemporary climate goals, as around 18.9% of current greenhouse gas emissions are attributed to energy use in buildings (2021) [2]. According to the Buildings Performance Institute Europe (BPIE), 97% of buildings in Europe are not energy efficient (2022) and the rate of energy renovation needs to drastically increase to reach climate neutrality by 2050 [3]. The need for smarter, more flexible and overall more energy efficient buildings calls for innovative approaches to improve building energy performance and enhance the operational efficiency, especially in the existing building stock. One promising area of development is the use of building digital twins, which presents a more holistic approach to building energy modeling. The digital twin concept includes three elements: a physical system, a virtual representation and a connecting data flow [4]. For building digital twins, a virtual building representation is used to emulate the behavior of chosen aspects of its physical counterpart, such as thermal development, air pollution, system operation and/or noise propagation. The digital twin functions as a multipurpose tool which, depending on its scope, may be used to monitor, evaluate, predict, optimize, and manage a building with automated continuous commissioning and operational strategy planning. The complexity and scope of building digital twins can vary greatly depending on their application. Some approaches, such as the framework proposed by [PERSON] [5], aim to provide a combined platform for handling a multitude of data types during the entire building life cycle including both design phases and operation/commissioning. Other approaches are more limited in their scope focusing exclusively on certain building aspects and phases, such as facilitating structural load calculations for building design and retrofitting, or supporting operation and commissioning of selected building energy systems. For effective widespread application in the current building stock, it is crucial that a digital twin framework enables a (mostly) automated setup of the virtual representation, ideally based on existing building documentation files such as building information modeling (BIM) files. It should also possess flexible modeling capabilities to be applicable across multiple building types and ensure optimal usage of the available building data. In this regard, a component-based modeling approach is advantageous, as it allows for flexible systems compositions and selection from several component models depending on available component specifications and measurements. A flexible building digital twin framework should incorporate fully and/or partially data-driven energy models as these are more easily scalable between buildings and typically provide better modeling accuracy than physics-based approaches [6]. In recent years, the use of digital twin approaches for both design and optimization in building energy simulation has accelerated [7]. The complexity, scope and application of building digital twin solutions vary greatly, but a majority of the literature consider modern buildings and rely heavily on existing building information, such as in the approach presented by [PERSON] et al., where an existing building information model (BIM) is used as a backbone for campus building modeling [8]. Many of the proposed approaches rely on EnergyPlus or similar energy modeling software [9]. The use of model predictive control (MPC) for building operation optimization has been widely suggested and explored within the heating, ventilation and air conditioning (HVAC) modeling literature [10] and it is often found to be the optimal control approach in control strategy comparisons [6].