## 1 Introduction  
This work builds upon our previous research [1] presented at the SOCO 2024 Conference, where we introduced an initial methodology for HVAC temperature modeling. In this expanded study, we refine our approach by incorporating additional experimental validation, providing a more detailed performance analysis and enhancing the core of this study, which is the proper definition of a feature selection and extraction methodology. The improvements presented here aim to further optimize energy efficiency and predictive accuracy in HVAC systems.  
In developed countries, buildings are responsible for an estimated 20-40% of total energy consumption with half of it being dedicated to maintaining thermal comfort [2]. HVAC systems encompasses all the devices related to the management and regulation of temperature, humidity, and air quality within enclosed spaces. More adaptive and flexible HVAC systems have been shown to achieve energy savings ranging from 12.4 and 17.4% [3] to as high as 25-32% [4], within the established indoor comfort conditions. Many studies have successfully attempted to improve energy efficiency in HVAC systems [3, 4, 5, 6, 7, 8], which goes hand-in-hand with proper thermal modeling of the studied system [4, 9, 10, 11, 12, 13, 14, 15, 16, 17]. Similar approaches have been explored for optimizing the energy consumption of wireless sensor networks, applying ultra-low power techniques and energy harvesting to enhance efficiency and prolong operational lifespan [18]. Numerous algorithms and software are used, and it is essential to decide which one to use to obtain a useful and accurate outcome with each system.  
This work proposes a novel preprocessing methodology grounded in expert knowledge for feature selection and extraction, specifically designed to enhance the predictive accuracy of temperature and energy consumption of HVAC data-driven models. The proposed approach is based on studying the thermodynamic interactions that describe the system to accordingly select the variables that have a meaningful impact on the studied system and creating dynamic variables that can contain information from multiple features to reduce the space of inputs of the data-driven model. The significance of this approach lies in understanding the thermodynamic interactions and the physical equations that describe these interactions, thereby exploring innovative criteria for feature selection and creation. Notably, the contributions of this paper include the utilization of real data from a non-residential building, providing a robust basis for model validation. Furthermore, the case study focuses on a complex HVAC system featuring a fan coil that extracts energy from an inertia tank. This unique setup underscores the general applicability of the proposed methodology, demonstrating its potential to address advanced HVAC configurations and improve predictive accuracy in various real-world applications.  
The rest of this work is organized as follows: Section 2 outlines the three primary families of HVAC modeling approaches and introduces support vector regression. Section 3 reviews the state-of-the-art in HVAC systems and data preprocessing techniques. Section 4 details the methodology, elaborating on the novel contributions to ensure reproducibility. Section 5 describes the building, the HVAC system used, its operational characteristics, and the application of the proposed methodology. Section 6 presents simulation results and analyzes the model's performance. The work concludes with a summary of key findings and suggestions for future research directions in Section 7.