# Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management
### Literature in Power Grids
Additionally, it addresses the challenges and opportunities in areas such as IoT, 5G technology, publicly available benchmarks, explainable AI, and federated learning, providing the necessity for technological data management advancements and support systems for future VPP initiatives. The AI-smart grids integration enhances flexibility, facilitates maintenance, and allows for adaptable energy distribution. Additionally, advancements in hydrogen energy, driven by data privacy legislation, grid modernization, and incentives, are discussed in [26]. The study in [27] presents future research related to smart grids and urban EMS, categorizingthese into physical, cyber, and social energy systems. The global climate crisis and the depletion of traditional energy sources have led to increased demand for RES. However, intermittent sources such as wind and solar cannot be fully regulated or pre-planned. To this end, ML is an emerging technology used to predict and detect faults in RES, thus reducing operational and maintenance costs. Techniques reviewed in ML are employed for fault prediction, early detection, and diagnosis, ensuring continuous power supply to loads, as indicated in [28]. Furthermore, over 10 regular algorithms for modeling and optimizing RES are discussed in [29], including ANN, LSTM, RNN, genetic algorithms (GAs), and particle swarm optimization (PSO). The findings are compiled from more than 100 studies conducted between 2020 and 2022. The review in [30] manifested distributed energy sources and their integration into utility grids, presenting microgrids as self-sustaining systems with distributed resources. It evaluates decision-making strategies, methods for quantifying uncertainty, communication technologies, and future directions for distributed energy resource integration in microgrids, while also providing insights into real-world applications. Advantages of Section 1.3 (Literature in Power Grids): The proposed literature in the power grids covered overviews of AI and ML applications in energy systems, including many benefits and drawbacks. One such benefit is that AI and ML models can optimize energy management via predictive maintenance, load forecasting, and anomaly detection, leading to improvements in efficiency, reliability, and operational flexibility. The implementation of these models can lead to data-driven decision-making in real-time and adaptive control for more advanced energy systems, including superior resources and lower operational costs. Scalability and application to a specific sector (e.g., microgrid integration, VPPs, and RES) are possible through hybrid modeling approaches that incorporate optimization with ML. Emerging technologies such as blockchain for decentralized energy management and AI for smart grid integration represent a potential step up too. Disadvantages of Section 1.3 (Literature in Power Grids): However, the studies also have limitations. Many AI and ML models face challenges with computational costs, high data demands, and difficulties in handling complex, real-time energy networks. Scalability remains an issue, especially when moving from small-scale implementations to larger grid applications. While some models excel in specific domains, such as load management or renewable energy forecasting, they often struggle with generalizability across different energy sectors. Furthermore, technologies, like blockchain, come with significant energy costs that can offset sustainability benefits. Lastly, despite the potential of predictive models, practical adoption is limited by having a few case studies and the lack of standardized frameworks, which restrict widespread implementation in real-world energy systems.