## 2 Methodology  
The development of the DT for monitoring and controlling the indoor air quality and energy aspects of the building was conducted through a structured methodological process, aimed at ensuring the effective integration of digital technologies with the operational needs of the facility. Initially, an in-depth analysis of the specific requirements was carried out, based on [PERSON] definition and the needs of the case study. This made it possible to identify the key parameters to be monitored, the required functionalities and the key performance indicators for assessing the effectiveness of DT. Next, the system architecture was designed, ensuring a high level of interoperability between devices and communication protocols. Suitable protocols were chosen for the transmission of environmental and energy data and integrated communication networks that would allow a stable connection between sensors, actuators and control systems. In parallel, an infrastructure for data collection and management was developed, implementing advanced platforms for information processing and visualization. The first step in creating the DT is to clearly define its objectives and functional scope. The primary goal is to monitor and optimize indoor air quality, energy consumption and overall environmental comfort, while ensuring seamless interoperability between hardware and software components. The system must support real-time data collection, predictive analytics through machine learning and automated control mechanisms. A network of IoT devices must be deployed to collect real-time environmental and energy data. Smart electricity meters and energy monitoring plugs will measure instantaneous and average electricity consumption, while dedicated air quality sensors will track parameters such as temperature, humidity, volatile organic compounds (tVOCs), particulate matter (PM2.5 and PM10) and CO\({}_{2}\) concentrations. Additional thermal comfort and lighting sensors will be installed to assess comfort metrics, such as Predicted Mean Vote (PMV) and Percentage of Dissatisfied (PPD). Occupancy detection will be achieved using motion sensors and camera-based people counting systems powered by computer vision algorithms. Actuators, such as automated window mechanisms or HVAC, will be integrated to dynamically respond to environmental changes. The interoperability of the system relies on a well-structured communication infrastructure. IoT devices and sensors will communicate using MQTT and HTTP protocols, while Modbus will be used for energy meters. The data transmission network will be based on a combination of wi-fi and ethernet, with local area network (LAN) connections providing stability for critical components such as energy meters and weather controllers. A central message broker, such as Mosquito MQTT, will facilitate real-time data exchange between devices. Data will be processed and stored on a dedicated infrastructure, ensuring long-term archiving and real-time availability. PostgreSQL and InfluxDB will handle structured and time-series data respectively, while lightweight databases such as SQLite can be used for local caching. A Linux-based server with multi-core CPU capabilities, at least 64 GB of RAM and a CUDA-enabled GPU will be required to efficiently process ML algorithms. Uninterruptible power supplies (UPS) will be required to maintain system stability in the event of power failures. Once the data collection system is in place, the next phase is to develop predictive models for energy consumption, indoor air quality and environmental comfort. ML techniques will be applied to historical data sets to train models capable of predicting trends and detecting anomalies. The system will use open source libraries such as TensorFlow, Scikit-learn and KNIME to develop predictive analytics pipelines. Models will be continuously refined through real-time feedback loops to improve accuracy and adaptability. Automation mechanisms will be implemented to optimize operational efficiency.