## 4 Results
### Key Parameters That Influence TCO and CWPs  
Regarding guiding theme 1--the key parameters that influence the TCO of CWP--we have compiled a list of parameters that could potentially impact TCO. These parameters include cooling load or cooling demand [6, 49, 50, 52, 86, 110, 112, 114, 118, 125], energy consumption and electricity price/tariff [3, 50, 57, 110, 111, 112, 118, 119, 121, 123, 127], meteorological data, such as temperature and weather data [6, 30, 57, 110, 111, 113, 118, 119, 120], cooling capacity or chiller size [6, 30, 48, 98, 113], COP [3, 11, 52, 126], occupancy variance [6, 123, 127], power consumption [48, 111, 118], part load ratio (PLR) [30, 118], and chiller type and configuration [6]. Figure 5 shows that most papers have considered cooling load/cooling demand as a fundamental parameter directly impacting the operational cost and TCO of chiller or HVAC systems [6, 49, 50, 52, 86, 110, 112, 114]. Energy consumption, electricity cost/tariff, and meteorological and weather data are the next most considered parameters [3, 50, 57, 110, 111, 112, 127]. The electricity cost and energy consumption are closely tied to this, as they directly correlate with the financial aspects of chiller system operations [6, 30, 57, 110, 111, 112]. Moreover, [PERSON] et al. [110] show that considering the impact of climate change on meteorological data is essential, as it directly affects cooling load and energy consumption. In contrast, parameterssuch as occupancy variance [6; 123; 127], power consumption [48; 111; 118], PLR [30; 118], and chiller type and configuration [6] are less frequently considered.  
Among them, some parameters can be obtained from other parameters. For instance, to accurately calculate energy consumption \(E_{i,j}\) (\(\text{kW-h}\)) as described in (5), it is essential to consider various parameters, including cooling load \(CL_{j}\) referenced in (8), system cooling capacity \(C_{i,j}\) (\(\text{kW}\)) shown in (6), part load ratio \(\text{PLR}_{i,j}\) also in (8), and Coefficient of Performance \(\text{COP}_{i,j}\) in (7) [52]. Also, the COP is an essential system efficiency measure, linking cooling load and energy consumption to provide insights into system performance [3]. Similarly, one of the key parameters in calculating maintenance cost is cooling capacity, as outlined in (9) and (10) in Section 2.  
Figure 4: A chart illustrating application domains or focus of TCO prediction and optimization-related works covered in this study.  
Figure 5: Parameters considered by different studies from 2017 to 2024.  
Various articles utilized or applied distinct parameters for distinct purposes, depending on their proposed models and objectives; for example, [PERSON] et al. [30] utilized nominal capacity, chilled water temperature, cooling water temperature, and PLR as inputs to predict COP and actual capacity, aiding in estimating energy consumption and reducing the TCO of the chiller. Meanwhile, [PERSON] et al. [6] used simulated data of cooling load distribution to optimize total cooling capacity and chiller numbers, leading to substantial life cycle cost (LCC) reduction. This optimization helps precisely match cooling output to actual demand, efficiently reducing energy waste and lowering life cycle costs. [PERSON] et al. [52] conducted statistical analyses on cooling load and COP uncertainties' effects on TCO. Sensitivity analyses, such as that by [PERSON] et al. [3], highlighted the critical impact of COP variations on LCC, emphasizing the need for efficient chiller systems to mitigate cost fluctuations.