# Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management
## 4 Artificial Intelligence and Machine Learning in EMS of Power Systems  
Physical modeling and analysis are the primary methods employed in power system investigations. Nevertheless, these conventional approaches are unable to effectively address the increasing complexity and uncertainty of contemporary systems. As a result, AI techniques, which are capable of self-learning and necessitate minimal reliance on mathematical models of physical systems, present optimistic solutions [171]. Large volumes of data are typically managed by power systems as a result of the integration of a variety of components, including ESS, communication infrastructures, smart meters, EVs, and DERs. Traditional computational methods frequently fail to effectively manage and process this vast amount of data. Consequently, to effectively address these challenges, AI methodologies are implemented in power systems. Overall, the utilized classes of AI models in power systems [172] are illustrated in Figure 9. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Collection** & **1/(day)** & **1/(1 h)** & **1/(0.5 h)** & **1/(0.25 h)** \\ \hline Records (billion) & 0.37 & 8.75 & 17.52 & 35.04 \\ Size (Terabyte) & 1.82 & 730 & 1460 & 2920 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Amount of collected GIS big data of power systems by annual one million metering devices. AI, at its highest level, covers all technologies and approaches that strive to replicate human intellect. ML is a major subset of AI that stands out for its capacity to enhance its performance over time by learning from data without the need for explicit programming. ML can be classified into three main categories: supervised learning, where models are trained using labeled data; unsupervised learning, which involves identifying patterns in unlabeled data; and RL, which concentrates on training agents to make decisions by maximizing rewards in an environment. In this regard, Figure 10 presents different applications and optimization fields of AI in power systems. As in Figure 10, AI in smart grids has been involved in several sectors, such as distributing workloads across many data centers to improve efficiency. Further, major studies have gone into reducing energy use and CO2 emissions from power stations as well as maximizing the effectiveness of new conservation methods. But there is still an unexplored potential of IoT sensors and energy-saving strategies with RL. Energy costs are the largest portion of total production costs in modern industry. As a result, control over  
Figure 10: Overall (**a**) applications of AI in smart grids and (**b**) the related optimization targets. Figure 9: Overall AI classification. energy costs is an utmost priority for decision-makers. By employing AI/ML methods, data can be used to create mathematical models that ensure that machines are activated only when necessary, decreasing energy consumption. By moving operations to periods when energy prices are cheap, this method reduces the overall demand for consumption as well as delivers considerable savings. Moreover, decreasing consumption makes it easier to avoid operating additional power plants during peak demand periods so energy generation can remain at lower CO2 emission levels. Therefore, since the importance and applications of AI/ML models are presented, the mostly used primary AI models and the power system are described, followed by Figure 11.  
Neural networks that were first introduced in [174] are computational models appeared by the structure and function of the human brain (according to Figure 11). They are composed of interconnected layers of nodes or neurons that transmit and process information. Each neuron receives input, processes it using a weighted sum and an activation function, and subsequently transmits the output to subsequent layers. The architecture typically comprises an input layer, one or more concealed layers, and an output layer.