## 5 Discussion
### Enhancing TCO Prediction Accuracy  
The current study reveals that conventional non-AI-driven methods, such as ARIMA [125], rule-based approaches [49; 111; 127], MPC [111; 127], and PID [49], have limitations, which have led to the adoption of AI-based algorithms to enhance the predictive modeling of parameters impacting TCO in CWPs. Previous studies have demonstrated the effectiveness of integrating AI in refining the predictive capabilities and operational efficiency of CWP systems [30; 86; 111; 112; 113; 127]. The review highlights how AI enhances the accuracy of the models. For instance, although ARIMA is a well-known conventional time series prediction technique, it struggles to capture the nonlinear and non-stationary characteristics commonly found in seasonal data, including cooling load, energy consumption, electricity prices, and weather patterns [125]. Similarly, MPC, a conventional control strategy that uses mathematical models to predict future system states, has drawbacks such as computational intensity and heavy reliance on accurate models, posing challenges in scalability and real-time applications [127]. In contrast, ML offers adaptability, handles complex nonlinearities and non-stationary characteristics without extensive modeling, and scales effectively with advancements in computing technologies [116; 125]. Therefore, the literature shows that ANNs have been integrated with MPC to enhance their accuracy [111; 127]. Additionally, PID controllers excel in systems with well-understood dynamics but struggle with nonlinearities and cannot adapt to changing conditions automatically [1]. ML methods, on the other hand, excel in complex, nonlinear environments by learning from data, offering adaptability and improved performance in dynamic settings. [PERSON] et al. [48]'s results demonstrated that ML outperformed PID controllers' performance. Like PID controllers, rule-based (RB) systems have limitations. RB systems lack flexibility and adaptability to new data [49], while ML models can learn and improve their performance when provided with additional data [48]. Moreover, RB systems need to be manually updated and maintained, which reduces their practicality. Due to their static nature and strict adherence to predefined rules, RB systems may be unable to identify and implement all possible optimizations within a system [111]. Thus, multiple research studies have compared their AI-based methods with RBC and demonstrated that their proposed ML and DL models outperformed RBC [49; 111; 127]. CWPs are dynamic and complex, with multiple states and inter-dependent variables exhibiting complex causal correlations [30]. Traditional non-ML approaches using historical averages or linear assumptions were less effective at predicting parameters like cooling load and energy consumption that exhibit nonlinear correlations influenced by weather, temperature, occupancy, and equipment efficiency [125]. In contrast, AI models can learn these complex nonlinear relationships from historical and real-time data [117; 122; 124]. In summary, while traditional methods have provided a foundational approach to managing CWPs, their limitations are evident [125]. AI techniques offer a robust alternative [123], enabling more accurate results and reducing the costs of CWP operations. To improve the accuracy of AI predictions, it is essential to accurately calculate key parameters defined in Section 2, such as energy consumption, cooling load, chiller capacity, and others. These values serve as the historical data used to train AI models, and their accuracy directly impacts the precision of future TCO predictions. Moreover, future research could benefit from developing advanced AI algorithms incorporating a wide range of parameters with multiple complex correlations and causations to derive more accurate models for TCO predictions in chiller plants.