## 4 Optimization Applications for SGs in Smart Manufacturing
### Applications Result Summary  
Through the survey on the applications of AI and SG technologies, we have discovered that these technologies offer effective energy solutions for smart manufacturing and factories. Our primary focus is on the tangible outcomes when AI and optimization SG are employed in the realm of smart manufacturing. We have organized the pertinent  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Optimization** & **Technology and** & **Reference** & **Note** \\
**Application** & **Implementation** & **Reference** & **Note** \\ \hline Energy cost & Machine learning, deep learning, algorithm, linear programming & [86; 87; 88; 89] & To effectively reduce the cost of energy use in smart manufacturing, AI technology is introduced into the SG for optimizing load control and power scheduling. Smart meters are implemented in the SG, and the measured data is analyzed by AI algorithms and models to manage power consumption more accurately. AI technologies are used to evaluate the reliability of SGs and simulate possible attacks on IoT-based energy networks to ensure a reliable energy system. The digital twin was established to provide an effective configuration and solution for the energy consumption of complex smart manufacturing systems. Big data from manufacturing is collected and analyzed by deep learning to control energy consumption and achieve sustainable development of the manufacturing process. & \\ QoS of communication networks and data collected & Controller, sensor & [100; 101; 102] & Consor and controllers are used for data collection to improve energy utilization and energy saving. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Related AI and SG technology application works in manufacturing.  
findings into Table 3, categorizing them based on the problems addressed, the methods and technologies employed, and the achieved results.  
From the compiled content, AI and SG-related technologies have brought a lot of results in the application of smart manufacturing, especially today's smart factories, which have faced high-mix and low-volume manufacturing models, IoT manufacturing areas, and the sustainability issues for 2050 net-zero environments, so that a lot of technical solutions are required to be addressed. From Table 3, SG scheduling and algorithms can improve energy efficiency and reduce electricity costs [2; 89]. To create uninterrupted electricity consumption, smart meters were used to replace conventional electricity meters, so as to avoid blackouts in advance [13; 22]. An integrated SG solution was implemented to increase manufacturing efficiency [85]. The AI technology was applied to improve power supply quality [66]. The AI for the equipment and manufacturing scheduling model with SG achieved energy cost savings [83; 88]. The optimization of digital twin for energy distribution through SG was proposed to effectively save electricity [96]. Big data communication and the data exchange power prediction model were used to improve energy usage prediction accuracy rate [19]. These practical works related to AI and SG applications in smart manufacturing allowed us to understand the applications of related technologies to effectively reduce energy use and carbon emissions for smart manufacturing factories, while effectively saving costs.