智能电网：架构要素、机器学习与深度学习应用及未来方向的综述
Smart Grid: A Survey of Architectural Elements, Machine Learning and Deep Learning Applications and Future Directions
AI总结：
本文综述了智能电网的架构要素以及机器学习和深度学习在其中的应用，并指出现有研究的局限性与未来方向。
摘要：
智能电网（SG）通常被称为下一代电力网络，作为21世纪不适应当前需求的电力系统的替代方案而出现。它集成了先进的通信和计算能力，因此有望以最小的影响提高能源分配的可靠性和效率。凭借其拥有的大规模基础设施以及系统中的底层通信网络，它带来了大量数据，这些数据需要各种技术来进行适当的分析和决策。大数据分析、机器学习（ML）和深度学习（DL）在分析这些海量数据并生成有价值的见解方面发挥着关键作用。本文在智能电网背景下探索并综述了智能电网架构要素、基于机器学习和深度学习的应用与方法。此外，在基于机器学习的数据分析方面，本文强调了当前研究的局限性，并指出了未来的发展方向。
英文摘要：
The Smart grid (SG), generally known as the next-generation power grid emerged as a replacement for ill-suited power systems in the 21st century. It is in-tegrated with advanced communication and computing capabilities, thus it is ex-pected to enhance the reliability and the efficiency of energy distribution with minimum effects. With the massive infrastructure it holds and the underlying communication network in the system, it introduced a large volume of data that demands various techniques for proper analysis and decision making. Big data analytics, machine learning (ML), and deep learning (DL) plays a key role when it comes to the analysis of this massive amount of data and generation of valuable insights. This paper explores and surveys the Smart grid architectural elements, machine learning, and deep learning-based applications and approaches in the context of the Smart grid. In addition in terms of machine learning-based data an-alytics, this paper highlights the limitations of the current research and highlights future directions as well.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
