Applications of artificial intelligence in predicting and managing energy consumption in smart grids and microgrids

Ahmad banisaeid¹

1) Master of Science in Electrical Engineering, Telecommunications System, Shahid Chamran University of Ahvaz

IOI Code : AADG-ABBFGB
Publication : The second international conference on new approaches in engineering and basic sciences(icnabs.ir/2nd)
Abstract :
    The global transition to renewable energy sources and the decentralization of power systems, along with the increasing growth of new electrical loads such as electric vehicles, have created unprecedented challenges in the sustainability, reliability, and efficiency of power grids. Smart grids and microgrids have emerged as key paradigms to address these challenges. In the meantime, artificial intelligence and machine learning, with the ability to process a huge amount of data generated by sensors and smart devices, have become vital tools for accurate forecasting and optimal energy management. This paper comprehensively and systematically reviews the applications of artificial intelligence in the key areas of energy consumption and generation forecasting, demand-side management, microgrid operation optimization, and improving grid stability. First, the framework of smart grids and microgrids and the role of big data in them are described. Then, a detailed classification of artificial intelligence algorithms is presented, including deep learning, reinforcement learning, artificial neural networks, and hybrid methods. In the following, practical applications of these algorithms in short-term and long-term load forecasting, variable renewable generation forecasting, optimal scheduling of distributed generation units, load response management, and electric vehicle integration are reviewed. Also, energy management strategies in islanded and grid-connected microgrids are analyzed using intelligent approaches. Important implementation challenges such as data quality, cybersecurity, scalability, and the need for interpretable models are discussed in detail. Finally, future trends such as the use of generative AI, federated learning, and the Internet of Energy are examined. The results of this study show that AI will act not only as an auxiliary tool, but also as the core of next-generation energy management systems, and will fundamentally transform the efficiency and flexibility of power systems.

Keywords : artificial intelligence smart grid microgrid energy consumption forecasting energy management