# Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management
### Literature in Power Grids  
Recent ML strategies for reliability and power energy systems control, specifically large-scale electric power systems, are reviewed in [8]. The study aims to unite current advances in the ML-related reliability literature while incorporating system-level management aspects through this study and offering use-cases for distribution grid, microgrid, and multi-energy systems. It makes the case that microgrids will be foundational for fulfilling worldwide energy needs and that grid decentralization is critical. Therefore, the need for intelligent and reliable EMS is mandatory to sustain their operation. AI in this regard provides solutions that are robust and scalable, powered by advanced algorithms. Accordingly, the comparison between the conventional and AI-based methods for EMS in microgrids is conducted in [9] looking at centralized, decentralized, and distributed systems. However, some privacy challenges of data, security, scalability, and explainability need to be solved. In [10], the recent ML approaches for EMS across residential and industrial sectors are discussed with the diversity of algorithms used, such as reinforcement learning (RL), DRL-based, and path integral-based control strategies. It evaluates the role in future communication technologies, addressing cyber-physical systems and IoT-based communication sensors, considering ML issues related to emergency measures for integrity protection, as well as its incorporation into existing control mechanisms. Moreover, Ref. [11] manifests hybrid methodologies by integrating optimization and ML techniques for clustering and classification with the perspective, which is trying to identify what challenges each methodology has so insights can be provided regarding the potential of their combination. The study in [12] examines the RES energy global adoption, such as solar and wind, emphasizing both their advantages and limitations. It discusses the growth of these RES, current trends, and emerging technologies. Given the high increase in the universal demand for energy, the development of new materials for RES technologies becomes considerably important. Traditional methods for material discovery are often expensive and time-consuming, which has led to the rise of ML as a research tool. In this context, Ref. [13] reviews ML approaches, algorithms, and advancements in material property prediction and developing energy-related materials. Lithium-ion batteries in ESSs, EVs, and portable electronics have challenges, such as overheating and aging, as they have high energy density. Battery management systems (BMSs) are essential for ensuring safe operation; however, accurate predictions performed on the state of health (SoH), state of charge (SoC), and remaining useful life are important too. To address this concern, Ref. [14] reviews ANN-based methods for predicting battery conditions, highlighting current trends and gaps in BMS applications. Additionally, physics-informed ML (PIML) techniques are gaining attention for improving model generalization, adherence to physical laws, and interpretability. Based on this, Ref. [15] provides an overview of PIML applications in subsurface EMS, with a study on seismic activities, reservoir simulations, hydrocarbon production forecasting, and intelligent decision-making. Considering other applications of AI/ML in EMS, forecasting is among the other important applications. The supplied/demanded power balance is important in the power energy dispatches. This balance is maintained through demand response programs, one in particular called short-term load forecasting (STLF). STLF with the integration of volatility and uncertainties in load demand by deep learning-based prediction has been considered for STSF [16], which emphasized accuracy and performance metrics that advance DL techniques, proposing future research directions related to online incorporation or operation, volatility robustness, and feasibility.