## 3 State of the Art
### HVAC Modeling Approaches  
As seen in Section 2.1, HVAC modeling approaches can be categorized into physics based, data-driven, and hybrid models, each with its own strengths and weaknesses.  
Physics-based models excel in generalization but have low prediction accuracy, and while they require little data, they are complex. Data-driven models can vary widely in both prediction accuracy and generalization but are simpler and data-intensive. Hybrid models strike a balance with high prediction accuracy, medium generalization, and moderate complexity and data requirements [19].  
Table 1 encapsulates the trade-offs and considerations for choosing between different modeling techniques in various applications, highlighting their strengths and weaknesses.  
Hybrid models have gained popularity due to their ability to combine the strengths of both physics-based and data-driven approaches. These models offer improved prediction accuracy, adaptability, and leverage physics-based simulations whi  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Modeling Technique** & **Weakness** & & **Strength** & **Research Studies** \\ \hline \multirow{4}{*}{Physics-based} & * Computationally expensive & * High accuracy when system & * High accuracy when system & \\  & * Requires detailed system information & * Initial analysis into underlying physical processesthe limitations of purely data-driven or physics-based models [43]. At the same time, data-driven approaches, particularly those utilizing machine learning and artificial intelligence, have seen significant advancements. These methods offer high adaptability and efficiency in cost-saving, with promising performance compared to other approaches [19].  
Modeling HVAC systems presents significant challenges due to their non-linear, dynamic, and multi-variable nature, making purely physics-based models difficult to implement effectively. Classical approaches rely on solving complex differential equations that require detailed knowledge of system parameters and are computationally intensive, making real-time applications impractical. Furthermore, HVAC performance is influenced by external factors like occupant behavior, weather conditions, and sensor variability, which are difficult to represent accurately with fixed physical models. In contrast, a data-driven framework leverages historical and real-time sensor data to automatically capture complex relationships between temperature dynamics, energy consumption, and external conditions without the need for explicit equations.  
Machine learning techniques excel in handling non-linear dependencies, allowing for improved forecasting accuracy and adaptability to changing conditions. Additionally, data-driven models require less computational power after training, enabling faster predictions compared to iterative numerical solutions of physical equations. Another advantage is scalability, as physics-based models must be redefined for each specific HVAC system, while machine learning models can generalize across different buildings by training on diverse datasets. For these reasons, a data-driven approach was chosen as it provides a more flexible, accurate, and computationally efficient solution for HVAC modeling and forecasting.