Electricity is one of the mandatory commodities for mankind today. To address challenges and issues in the transmission of electricity through the traditional grid, the concepts of smart grids and demand response have been developed. In such systems, a large amount of data is generated daily from various sources such as power generation (e.g., wind turbines), transmission and distribution (microgrids and fault detectors), load management (smart meters and smart electric appliances). Thanks to recent advancements in big data and computing technologies, Deep Learning (DL) can be leveraged to learn the patterns from the generated data and predict the demand for electricity and peak hours. Motivated by the advantages of deep learning in smart grids, this paper sets to provide a comprehensive survey on the application of DL for intelligent smart grids and demand response. Firstly, we present the fundamental of DL, smart grids, demand response, and the motivation behind the use of DL. Secondly, we review the state-of-the-art applications of DL in smart grids and demand response, including electric load forecasting, state estimation, energy theft detection, energy sharing and trading. Furthermore, we illustrate the practicality of DL via various use cases and projects. Finally, we highlight the challenges presented in existing research works and highlight important issues and potential directions in the use of DL for smart grids and demand response.

中文翻译：

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智能需求响应和智能电网的深度学习：综合调查

电力是当今人类的必需品之一。为了解决通过传统电网传输电力的挑战和问题，智能电网和需求响应的概念应运而生。在此类系统中，每天从各种来源生成大量数据，例如发电（例如风力涡轮机）、输电和配电（微电网和故障检测器）、负载管理（智能电表和智能电器）。由于大数据和计算技术的最新进展，可以利用深度学习（DL）从生成的数据中学习模式并预测电力和高峰时段的需求。基于深度学习在智能电网中的优势，本文对深度学习在智能电网和需求响应中的应用进行了全面的综述。首先，我们介绍了 DL、智能电网、需求响应以及使用 DL 背后的动机。其次，我们回顾了深度学习在智能电网和需求响应中的最先进应用，包括电力负荷预测、状态估计、能源盗窃检测、能源共享和交易。此外，我们还通过各种用例和项目说明了深度学习的实用性。最后，我们强调了现有研究工作中提出的挑战，并强调了将深度学习用于智能电网和需求响应的重要问题和潜在方向。