## 5 Conclusions  
In future smart communities, buildings are required to possess energy-efficient systems, offer optimal comfort, empower occupants with information, adapt operations based on energy grid conditions, and facilitate strategic maintenance [44]. With the latest transformations and advancements in the building sector, it is anticipated that digital technologies will play a significant role, resulting in intelligent, sustainable, efficient, reliable, and interconnected buildings worldwide. The remarkable progress in the fields of data, analytics, and connectivity is enabling the emergence of various new digital smart applications and devices. Digitalization and the availability of digital information are the primary catalysts that are rapidly permeating different aspects of the building sector and the construction industry. The majority of ongoing retrofit endeavors and activities follow a per-building strategy, utilizing static and tabulated methods and tools that rely heavily on broad assumptions and generic inputs. This approach obstructs the realization of the retrofit process's optimal potential and added value. Furthermore, retrofits are carried out in a reactive manner, where modifications are made when required based on user feedback. This lack of a systematic retrofitting approach resulted in significant disparities between the projected savings and the actual savings achieved post-retrofit. However, these methods are not only costly but also consume substantial time and resources. To tackle these challenges, this research introduces a digital and scalable solution (DanRETwin) that can be effortlessly tailored to diverse scenarios and structures with minimal adjustments. The suggested approach is founded on data gathered from an array of affordable, clamp-on IoT sensors and meters, which are seamlessly and wirelessly connected and integrated. These sensors allow users  
Figure 7: The step response testing results of a heating radiator system using AUSTET tool [44]. to easily monitor parameters such as power, flow, heating, and cooling by attaching sensor units externally to cables and pipes, eliminating the need for intricate technical knowledge or system disconnection. Advanced mathematical AI models are employed, rendering elaborate hardware unnecessary. The data from various IoT devices is then structured and harnessed to construct robust and adaptable energy models for different systems and components. These models constitute a repository from which a variety of components can be chosen to suit diverse scenarios and applications, enabling efficient, adaptable, and effective utilization of time and personnel. Furthermore, the solution incorporates an optimization approach into the digital twin framework to select the most efficient and cost-effective modification solutions for specific building enhancements. The proposed solution will impact and improve energy efficiency in the building sector and provide added value throughout the entire tool chain. Building owners and clients will have more energy-efficient and comfortable buildings that are optimally retrofitted and well-recommissioned, with better indoor comfort and operational performance from day one and throughout the building life cycle. Facility and building managers will be able to harness DanRETwin's services of advanced monitoring and systematic retro-commissioning on their newly retrofitted buildings and facilities, allowing timely intervention in the case of failures or faults, as well as data-driven performance optimization on different building levels. Designers, energy consultants, and contractors will be employing the services provided by the proposed digital twin as a potential offering to retrofit, enhance, and optimize clients' buildings performance and to inform optimal decisions.