Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time decisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and preferences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area.

近年来，人们对需求响应（DR）作为提供灵活性并以此以节省成本的方式提高能源系统的可靠性的一种方式越来越感兴趣。然而，与灾难恢复相关的任务的高度复杂性，再加上大规模数据的使用以及对近实时决策的频繁需求，意味着人工智能（AI）和机器学习（ML）–人工智能的一个分支-最近已成为实现需求方响应的关键技术。人工智能方法可以用来应对各种挑战，包括选择最佳的消费者群体进行响应，学习他们的属性和偏好，动态定价，设备的调度和控制，学习如何激励DR方案的参与者以及如何奖励他们。以公平和经济有效的方式。这项工作基于对160多个论文，40个公司和商业计划以及21个大型项目的系统评价，概述了用于DR应用的AI方法。根据使用的AI / ML算法和能量DR中的应用领域对论文进行了分类。接下来，将介绍商业计划（包括初创公司和老牌公司）以及大规模创新项目，其中将AI方法用于能源DR。本文最后讨论了针对不同DR任务的已审查AI技术的优势和潜在局限性，并概述了在这一快速增长领域中未来研究的方向。根据使用的AI / ML算法和能量DR中的应用领域对论文进行分类。接下来，将介绍商业计划（包括初创公司和老牌公司）以及大规模创新项目，其中将AI方法用于能源DR。本文最后讨论了针对不同DR任务的已审查AI技术的优势和潜在局限性，并概述了在这一快速增长领域中未来研究的方向。根据使用的AI / ML算法和能量DR中的应用领域对论文进行了分类。接下来，将介绍商业计划（包括初创公司和老牌公司）以及大规模创新项目，其中将AI方法用于能源DR。本文最后讨论了针对不同DR任务的已审查AI技术的优势和潜在局限性，并概述了在这一快速增长领域中未来研究的方向。