# Research on Real-Time Control Strategy for HVAC Systems in University Libraries
## 3 Experimental Cases
### Limitations of the Method  
Compared with other algorithms, the LM-UGO offers several advantages. It demonstrates a fast convergence speed and avoids becoming trapped at the local minimum points. In addition, it enables the total error of the system to quickly reach the required accuracy level, has a short training time, and provides more accurate data prediction. The LM-UGO also exhibits strong fault tolerance and high reliability. In scenarios with multiple influential factors (i.e., more input nodes), the network prediction established by the LM-UGO achieved high accuracy and rapid speed, making it suitable for applications with stringent real-time requirements. Therefore, the LM-UGO has promising prospects for application. However, owing to its substantial computational and storage requirements, a relatively high-performance computer is necessary when utilized in large complex networks. Moreover, when excessive weight is involved, the applicability of the LM algorithm may not be guaranteed; therefore, further research is required.  
The findings of this study have certain limitations. First, the primary focus of this study was on public buildings, specifically university buildings, which display functional diversity. Owing to the unique characteristics of these buildings and the movement of personnel within the university setting, the control strategy for building HVAC systems also varies. While the findings may provide some reference value for public buildings such as shopping malls, adjustments to the HVAC system control strategy are necessary based on specific building functionality and personnel mobility. The current findings are based on a library building case study, which limits their applicability to other building types. Subsequently, the study will be extended to other library buildings or other building types to improve generalizability.  
Furthermore, it is important to consider the climatic conditions under which the experiments were conducted. The location has a temperate continental climate characterized by long, dry, and cold winters, hot and humid summers, rapid warming in spring, rapid cooling in autumn, short and windy spring and autumn seasons, and distinct dry and wet periods. In addition, the differences in climatic conditions and altitude may not translate well to areas with different weather patterns and need to be further explored.  
Another key aspect not considered in current models is the impact of the architectural characteristics of the building and its thermal behavior. Factors such as building materials, insulation, and overall building design can have a significant impact on the thermal performance, which in turn affects the efficiency of the HVAC system. In addition, the impact of solar radiation on the thermal dynamics of buildings, including on heating and cooling demand, has not been considered in the current model. The reported energy savings are based primarily on data from this experimental program rather than reliable third-party validation. A follow-up study will conduct multiple experimental projects to demonstrate the experimental effectiveness of the methodology.