## 1 Introduction
### Literature Review  
The workplace is a complex system in which the physical space and its occupants interact dynamically. It is crucial to evaluate the performance of the workplace by understanding how it supports workers' activities and meets their needs. Many studies have examined the impact of various physical and non-physical factors on worker satisfaction, productivity and well-being [9]. To understand how these factors affect workers, it is necessary to collect and integrate both the objective characteristics of buildings and the subjective conditions of workers. New approaches, such as the use of smartphone apps and wearable devices, are replacing traditional methods, such as post-occupancy surveys to collect real-time feedback from occupants [10]. Occupant-centered workplace management requires a system that integrates data generated by buildings and by workers themselves. Recent studies have demonstrated the potential of using BIM, sensor networks and semantic web technologies for this purpose [4,11]. However, specific solutions for the management of indoor work environments are still under development. [PERSON] et al. presented an innovative system for night-time occupancy detection in university buildings, using an algorithm based on the brightness of lights instead of additional sensors. This method exploits existing surveillance cameras, achieving 98.67% accuracy and improving building management. In addition, it introduces a 3D GIS model based on photogrammetry and laser scanning for a more accurate representation of interior spaces. The study offers an effective and cost-efficient solution to optimize campus security and energy efficiency, with potential applications in other building contexts [12]. [PERSON] et al. explored an occupant-centric approach to monitoring and improving indoor environmental quality (IEQ) in a high-rise office building in Hong Kong using an IoT sensor network. For 15 months, real-time data was collected from 12 points in the building, monitoring parameters such as temperature, humidity, air quality, lighting and noise. This method, which is more accurate than traditional periodic surveys, made it possible to analyze instantaneous environmental changes and improve space management, contributing to occupant well-being, comfort and productivity. The study highlights the importance of intelligent, tailored solutions to optimize both the human experience and energy efficiency in buildings [13]. [PERSON] et al. proposed an advanced home energy management system (EMS) to optimally integrate electric vehicles (EVs) and photovoltaic (PV) systems to improve grid sustainability and reduce energy costs for homeowners. The method uses an optimization strategy based on time-of-use (TOU) tariffs, household consumption patterns and EV variables, such as charging times and battery status. The system operates in two phases: one based on solar availability and the other on PV generation forecasts. The results show a significant reduction in energy costs and improved electricity stabilization, demonstrating that homes with PV and EVs perform better than homes with EVs only [14]. [PERSON] et al. proposed an innovative approach to improve energy efficiency in the home by integrating a digital twin (DT) on the home assistant platform. Using IoT devices and sensors, the system creates a virtual replica of the home environment, providing real-time data on energy consumption, personalized suggestions for savings and an advanced occupancy detection mechanism based on machine learning (ML). Tested in the lab, the system achieved 95.12% accuracy in occupancy detection and 80% positive feedback for its recommendation system. The results demonstrate the potential of DT technology to reduce energy waste and costs, promote sustainable practices and improve user comfort in smart homes [15]. The integration of BIM with static and dynamic data has become increasingly important in the AECO sector, leading to the emergence of the DT concept [16; 17; 18; 19].