## 1 Introduction
### Literature Review  
The optimization of energy efficiency and sustainability in building operations has been extensively studied through established frameworks, such as the Building Research Establishment Environmental Assessment Methodology (BREEM) [8]) and Leadership in Energy and Environmental Design (LEED) [9]. Comparative analyses of these methodologies have identified their respective strengths and limitations, particularly in terms of regional adaptability and cost effectiveness [10; 11; 12]. [PERSON] et al. examined strategies for achieving LEED certification across 1500 buildings in the United States, providing insights into optimizing certification processes to align with project owner expectations [13]. Recent advancements have introduced BIM tools designed to evaluate LEED credits for diverse construction alternatives, enhancing the accuracy of sustainability assessments [14; 15]. HVAC technologies, particularly heat pumps (HPs) and Variable Refrigerant Flow (VRF) systems, have emerged as key solutions for minimizing energy consumption and enhancing sustainability in buildings [16]. VRF systems, known for their adaptability and efficiency under varying load conditions, have been extensively analyzed through energy modeling, performance optimization, and predictive analytics. [PERSON] et al. employed Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and Auto-Regressive Integrated Moving Average (ARIMA) models to predict VRF system energy consumption in office buildings, determining that ANN-based models exhibited the highest predictive accuracy [17]. Economic feasibility studies by [PERSON] et al. emphasize the cost effectiveness of VRF/heat pump solutions in five-story office buildings in Greece, showcasing their role in achieving cost optimality and nearly Zero-Energy Building (nZEB) status. The integration of Photovoltaics (PVs) was found to be crucial for reaching nZEB thresholds, with VRF systems playing a pivotal role in achieving sustainability goals [18]. Further experimental and simulation-based research by [PERSON] et al. on Variable Water Volume (VWV) and Air Source Heat Pump (ASHP) systems demonstrated high seasonal energy efficiency, with Seasonal Performance Factors (SPFs) surpassing 3.0. Their findingspropose system enhancements based on real-time operational data, ensuring optimal HVAC performance [19]. The role of occupancy monitoring in energy optimization has gained increasing attention. [PERSON] et al. analyzed 2700 residential units in Jaipur (India) to identify determinants influencing EPC ratings [20]. Additional studies have examined discrepancies between BIM-modeled and actual energy consumption data in high-performance buildings [21; 22; 23]. [PERSON] et al. provided empirical evidence comparing real versus BIM-predicted energy consumption in a nearly zero-energy office building [24]. Occupancy-driven energy management has been explored by [PERSON] et al., who applied artificial neural networks to monitor occupancy rates in an office building in Tehran, Iran, revealing a strong correlation between occupancy levels and energy consumption [25]. Similarly, multi-sensor fusion techniques have achieved a 23.5% reduction in energy usage in an academic office building in Wuhan, China, demonstrating the potential of real-time optimization strategies [26]. Recent simulations by [PERSON] et al. incorporating detailed agent movement trajectories into electricity consumption models yielded predictions 19% closer to real-world energy use, highlighting the necessity of high-fidelity occupancy tracking for accurate forecasting [27]. The future of HVAC technology is increasingly centered on HP and VRF systems, with research consistently showcasing their superior energy efficiency and environmental benefits [28; 29; 30; 31]. [PERSON] et al. advanced predictive modeling for VRF energy consumption in office environments [17], while [PERSON] et al.