# Research on Real-Time Control Strategy for HVAC Systems in University Libraries
### Literature Review
[PERSON] [19] focused on the joint optimization of HVAC systems with building-integrated photovoltaic/thermal (BIPV/T) systems. The optimization of HVAC integration through model predictive control emphasizes the importance of thermodynamic applications to BIPV/T systems. [PERSON] and [PERSON] [20] investigated HVAC solutions in low-energy apartment buildings to improve their energy efficiency and cost-effectiveness, providing practical and cost-effective solutions. [PERSON] [21] explored a reinforcement learning optimization methodology for HVAC systems, which achieved comparable improvements in energy consumption and maintained the baseline level of thermal comfort through online learning. [PERSON] and [PERSON] [22] used a PSO-based and Extreme Learning Machine (ELM) hybrid model to predict the indoor temperature and energy consumption of HVAC systems. The LM algorithm is an advanced optimization technique that integrates the gradient descent method with the Gaussian Newton method, the latter serving as an enhanced version of the former [23]. This approach not only retains the local convergence property of the Gaussian Newton method but also incorporates the global advantages of gradient descent, making it effective for minimizing the sum of squared errors. Due to its rapid convergence, moderate computational requirements, and ability to manage constraints, the LM algorithm is a favorable choice in real-world applications. [PERSON] et al. [24] introduced a parameter extraction and optimization technique for the EKV model using the LM algorithm, achieving excellent fitting precision. To mitigate vibrations in adaptive optics systems, the LM algorithm was employed for parameter identification, leading to considerable vibration reduction and a significant decrease in the residual vibration's root mean square, bringing it down to micro-radian levels [25]. While the LM algorithm shows substantial improvements over earlier optimization methods, it can still encounter issues with local optima in certain scenarios [26]. Based on this, the 1 stOpt software (version 10.0) integrates its unique universal global optimization algorithm (UGO) in nonlinear curve fitting, which has been widely used in many fields, such as water conservancy and hydropower engineering, energy, and the environment, and has achieved very good fitting prediction results. However, considering the exclusive rights of software, this algorithm has not yet been published. We used a combination of two algorithms to fit and sort the correlation of the runoff impact factors. In building HVAC systems, the actual operation data of the system is the basis of energy-saving control, and the models of different buildings and HVAC systems vary greatly; therefore, the algorithm model needs to be optimized online when the energy-saving algorithm is running. In summary, researchers have mainly studied HVAC operation control strategies in the context of cold and heat source systems, chilled water systems, and cooling water systems. Although it is known that each HVAC system has an obvious energy-saving effect under different operation control strategies, less research has been carried out on these systems, and the relationships between HVAC systems have so far been neglected. This study intends to investigate an operational strategy for the entire HVAC system based on the operation control of each HVAC system.