## 4 Results
### AI in Forecasting TCO and Saving Costs
#### 4.3.1 AI Algorithms in TCO Prediction  
AI algorithms have been primarily utilized for two key purposes, cost forecasting and optimization, aimed at achieving financial goals. We categorized these AI algorithms according to their criteria: (a) ML, (b) DL, and (c) probabilistic and heuristic methods, as shown in Figure 6.  
Figure 6: List of individual AI methods employed (along with the number of publications) from 2017 to 2024. For improved forecasting, various studies have incorporated ML and DL algorithms to learn intricate time series patterns of the parameters, which includes ML algorithms such as Support Vector Machines (SVMs) [116]; tree-based and ensemble ML algorithms such as decision tree [57], AdaBoost Regressor [110], XgBoost [110], and Extra Tree Regressor [110]; and DL algorithms such as Artificial Neural Networks (ANNs) [30,86,111,112,113,120,121,126,127], Convolutional Neural Networks (CNNs) [115], Long Short-Term Memory (LSTM) [46,49,50,114,115], Deep Reinforcement Learning (DRL) [50,112,114], and Deep Q-Learning [119], among others. Moreover, some studies have developed hybrid methods [46,48,115,116,117,119,125,117] that incorporate multiple advanced ML and DL algorithms, such as combining temporal convolutional networks (TCNs) with bidirectional LSTM [115], merging reinforcement learning and neural networks [119] and integrating back-propagation neural networks with LSTM [48], or merged statistical and ML strategy (e.g., autoregressive with exogenous [117]), to solve nonlinear challenges. Some studies employed heuristic and optimization methods, such as Genetic Algorithms [110,113,126], Particle Swarm Optimization (PSO) [30], Salp Swarm [121], and Proximal Policy Optimization (PPO) [112]. Probabilistic methods, such as Hidden Markov Processes and Monte Carlo Simulations [110], are utilized in this field. These probabilistic methods can be integrated into AI systems to handle uncertainty and make informed decisions based on probability distributions, thereby enhancing the robustness and adaptability of AI algorithms. The majority (approximately 52%) incorporated DL algorithms for parameter forecasting. In addition, hybrid AI approaches, which combine multiple ML and DL algorithms, were used in about 24% of the studies. ANNs and LSTM are the most frequently used DL algorithms for predicting TCO parameters. Nine articles focus on the ANN [30,86,111,112,113,120,121,126,127], while five articles individually explore LSTM [45,48,49,112,113,112]. Among ML algorithms, tree-based ensemble algorithms are mostly preferred [57,110]. Additionally, for optimization, DRL algorithms are predominantly considered [50,112,114] because of their ability to solve complex decision-making problems by interacting with an environment and receiving rewards or penalties based on actions. Our findings indicate the growing trend of employing ML and DL techniques, particularly for forecasting energy consumption within HVAC and CWP systems, as energy consumption is the major driver of operating cost and TCO [30,49,50,57,86,110,112]. For instance, [PERSON] et al. [30] proposed an ANN-based model to predict the energy consumption to minimize hourly energy consumption in the chiller plant, which led them to optimize the LCC. Similarly, [PERSON] et al. [86] proposed a deep neural network model to predict energy consumption based on historical energy consumption data. [PERSON] et al. [57] developed a fault diagnosis model using a decision tree regressor to save energy consumption. Moreover, a trend is observed in the literature regarding the size of datasets, which range from as few as 58 instances [113] to over 10,000 [110,115,117], depending on the study's scope and the data availability. For example, the study of [PERSON] et al. [110] utilized a dataset of 11,001 instances from different climate scenarios to assess HVAC system performance. In contrast, other studies have had to rely on smaller datasets, often fewer than 4000 instances, derived from specific simulations or limited real-world observations [3,6,52,86,113,122].