# Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management
### Literature in Power Grids
In [17], a unified EMS is presented for energy management and interhousehold sharing with the help of ML and blockchain. Using ML to forecast energy generation and building a bidding system on top of it in order to distribute this automated power through peer-to-peer (P2P) transactions. It not only balances supply/demand, but it also supports prosumer-consumer direct energy trading, which incurs more computational complexity while still having better performance in terms of balancing charges, workload division, and capital costs. The study in [18] reviews district heating and cooling networks, smart EMS, and control strategies through multidisciplinary approaches, considering both heating and cooling aspects. It proposes an energy migration framework for intercity transportation to facilitate energy sharing and regional balance. The study also emphasizes the technical feasibility of ML methods for energy planning and optimization. A district energy network is modeled using RES, waste heat recovery, and diversified energy storage systems (ESSs). Future research should focus on addressing technical challenges, establishing benchmarks, managing energy congestion, and incorporating multicriteria decision-making. The depletion of fossil fuels has triggered an energy crisis, making RES essential. As discussed in [19], EMS strategies are mostly taken into account for improving power system reliability and mitigating climate change risks, with EMS as the important component. The study also manifests the importance of AI in enhancing smart grid technologies and managing load uncertainties. Another study in [20] reviews 135 publications on offshore wind energy, turbine control, wind farm wake management, and layout optimization. It examines AI technology applications, such as fuzzy logic, DL, and RL. Additionally, Ref. [21] reviews ML-based predictive maintenance in microgrid systems, highlighting how to enhance efficiency, safety, and sustainability. The study reviews recent techniques, identifies gaps, and recommends future research directions. The role of AI in power distribution systems is reviewed in [22] about electrical power load forecasting, fault detection, and demand response strategies. This study is presented on selecting appropriate AI models while evaluating the pros and cons of various learning methods. It also addresses the incentive and price-based demand response program implementation, taking into account control objectives, input sources, and applications. DC microgrids are now considered significant due to their adoption, solar PV systems, and energy storage technology integration, alongside their challenges related to frequency and reactive power control. Key factors for a stable and reliable DC microgrid include controlling DC bus voltage, power management, and SoC restoration. Control strategies for DC microgrids are considered using centralized/decentralized approaches in addition to distributed, multi-level, as well as hierarchical systems, along with advanced methodologies of nonlinear control, robust control, model predictive control (MPC), and AI-based methods. The study in [23] analyzes these elements by identifying research gaps and the directions for the need for additional development. Research presented in [24] investigates PV system utilization, desalination technologies, AI, and ML in water desalination processes. It assesses recent advancements, identifies trends, and explores the influence of AI and ML on PV system performance, maintenance, and monitoring. The significance of developing virtual power plants (VPPs) is considered in [25], approached from a data-centric perspective that analyzes energy management across the data lifecycle. This study reviews advancements in data mining, advanced communication technologies, decision support methods, security and privacy, RL, P2P sharing, blockchain, and market participation.