# Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management
## 4 Artificial Intelligence and Machine Learning in EMS of Power Systems
Regarding RL [180, 181, 182] (stated in Figure 11c), it is an ML paradigm that emphasizes the training of agents to make a series of decisions by interacting with an environment. In real life, an agent acquires the ability to accomplish an objective by executing actions and receiving feedback in the form of rewards or penalties. The agent's goal is to maximize the cumulative reward over time by utilizing known information and investigating the environment to make optimal decisions. Typically, Markov decision processes are employed to represent the learning process. In this model, the agent observes the current state, takes an action, and transitions to a new state, resulting in a reward. RL algorithms, including DRL, policy gradients, and Q-learning, employ a variety of techniques to approximate the optimal policy or value function. The capacity to learn from interaction and improve over time is essential in various disciplines such as robotics, game playing, autonomous vehicles, and recommendation systems where RL is applied, especially in power systems, as demonstrated in Figure 12.  
Consequently, RL is mostly applied to buildings, markets, and the power grid. The buildings section emphasizes the role of RL in the optimization of multifarious building systems to improve occupant comfort and energy efficiency of buildings. This encompasses comfort illumination, which entails the adjustment of illumination levels to ensure that they meet comfort standards while simultaneously conserving energy. It is essential to manage temperature to ensure a comfortable indoor environment by effectively balancing the heating and cooling burdens. HVAC systems are managed to optimize thermal comfort and air quality while reducing energy consumption. The Indoor Air Quality aspect guarantees that the air within buildings is both safe and pure, taking into account factors such as ventilation and filtration. Finally, energy efficiency incorporates all strategies and technologies that are designed to decrease the overall energy consumption of buildings, enhancing their sustainability. The market section includes the economic components of the energy system, where RL can be instrumental in optimizing market operations. The day-ahead market is a mechanism that enables market participants to offer and plan their operations by planning and trading electricity one day in advance. The spot market is a real-time market in which electricity is purchased and sold in accordance with the current supply and demand conditions. Nash equilibrium is a concept in game theory that describes a stable state in which market participants are unable to profit from unilaterally altering their strategy. RL can be employed to predict market behavior, identify optimal bidding strategies, and guarantee the stability and efficacy of market operations. Also, the electrical grid is a manifestation of the technical aspects of grid management, and RL has the potential to improve grid stability and reliability. The regulation of voltage and frequency is necessary to guarantee a consistent power supply by ensuring that they remain within permissible limits. Cybersecurity is essential for safeguarding the infrastructure from cyber threats and guaranteeing the integrity of data and control systems. The power system's capacity to endure and recuperate from short-term disturbances is referred to as transient stability. The optimal charging and discharging of ESS is contingent upon the administration of SoC. This ensures the longevity of the batteries and the reliability of the system. OPF is the process of determining the most efficient method of routing electricity through the grid to meet demand while minimizing losses and costs. Finally, on the side of dispatch as an act of operation in buildings, power grids, and its seen effect in the market, it is concerned with the optimization and management of energy resources in accordance with supply and demand conditions.