## 4 Results
### AI in Forecasting TCO and Saving Costs
#### 4.3.2 Cost-Saving Outcomes of AI-Driven Solution  
This section highlights the impact of cost and energy savings outcomes in recent state-of-the-art studies of AI-driven solutions. As chiller plants and HVAC systems account for the majority (more than 50%) of the energy consumption of building facilities [120], the literature studies indicate that a substantial portion of the papers focused on reducing energy consumption and operating costs for these systems [30, 49, 50, 111, 112, 116, 127]. Table 2 comprehensively summarizes recent research findings using AI on cost and energy savings outcomes across various studies. A consistent trend across the literature is the optimization of critical parameters, such as energy consumption, cooling load, chiller capacity, and COP, which has led to substantial improvements in both energy savings and cost reduction [11, 112, 116, 118, 111, 30, 48, 49, 50, 111, 112, 116]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Author (Year)** & **Outcome** & **Methods** \\ \hline [PERSON] et al. [127], 2020 & Reduced the operating cost by 3.4\% & ANN \\ \hline [PERSON] et al. [48], 2020 & Reduced energy consumption by 11.5\% and & LSTM \\  & operating costs by 7.5\% & \\ \hline [PERSON] et al. [112], 2021 & Reductions in electricity costs (3\% to 17\%) & DRL \\  & and CO\({}_{2}\) emissions & \\ \hline [PERSON] et al. [50], 2021 & Reduced electricity costs by 3\%, decreased & \\  & peak energy demand by 23\%, and lowered & DRL \\  & the peak-to-average ratio (PAR) by 20\% & \\ \hline [PERSON] et al. [111], 2022 & Reduced the operating cost by 9.1–14.6\% & ANN \\ \hline [PERSON] et al. [49], 2023 & Improved energy savings by 9\% & LSTM \\ \hline [PERSON] et al. [30], 2023 & Reduced life cycle cost (LCC) by an average & ANN \\  & of 17–18\% & \\ \hline [PERSON] et al. [116], 2023 & Reduced total LCC by 7.1\% & Hybrid (SVM + ANN) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cost optimization outcomes’ summary of AI methods across selected studies. Among the studies, the most notable energy savings were reported by [PERSON] et al. [30], with an estimated reduction of 18% in total LCC. Additionally, studies conducting AI-based model predictive control (MPC) strategies have demonstrated notable reductions in OPEX. For example, [PERSON] et al. [111, 127] enhanced their AI-based model predictive control (MPC) strategy over their 2020 and 2022 studies, achieving substantial operating cost reductions. Initially, they integrated ANN with MPC, utilizing the \(\varepsilon\)DE-RJ (epsilon-constrained differential evolution with random jumping) optimization solver and analyzing parameters such as occupancy variation and electricity flow, which led to a 3.4% cost reduction. Their 2022 follow-up incorporated improvements to the ANN models and extended their analysis to include several chiller components' temperature and power consumption. These enhancements and their refined \(\varepsilon\)DE-RJ strategy resulted in operating cost reductions of up to 14.6%. In addition, [PERSON] et al. [49] demonstrated that minimizing cooling load demand impacts energy savings by using the popular deep learning algorithm LSTM to predict future cooling loads. Deep reinforcement learning (DRL) has also emerged as a promising approach, particularly in reducing electricity costs. Both [PERSON] et al. [112] and [PERSON] et al. [50] applied DRL algorithms, with [PERSON] et al. [112] achieving notable savings by optimizing the control strategy of an industrial cooling system, while [PERSON] et al. [50] integrated LSTM with DRL to optimize electrical demand profiles, leading to reduced peak energy demand and electricity costs. Although DRL algorithms offer advantages in handling complex systems and dynamic environments, challenges such as instability during training and the need for extensive training episodes were noted [114]. Hybrid AI approaches that combine multiple ML and DL algorithms have shown the potential to enhance system performance. For instance, [PERSON] et al.