论文标题
电力市场代理的机器学习应用程序：系统文献综述
Machine learning applications for electricity market agent-based models: A systematic literature review
论文作者
论文摘要
电力市场在能源系统的脱碳中起着至关重要的作用。但是，电力市场由许多不同的变量和数据输入组成。这些变量和数据输入的行为有时是不可预测的，无法预测a-priori。因此，有人建议将基于代理的模拟用于更好地了解电力市场的动态。基于代理的模型提供了整合机器学习和人工智能以增加智能，更好地预测并以更好，更有效的方式控制电力市场的机会。在这篇系统的文献综述中，我们回顾了2016年至2021年之间发表的55篇论文，这些论文的重点是应用于基于代理的电力市场模型的机器学习。我们发现围绕流行主题的研究集群，例如招标策略。但是，存在大量不同的研究应用程序，这些应用程序可能会受益于受调查的应用程序的高强度研究。
The electricity market has a vital role to play in the decarbonisation of the energy system. However, the electricity market is made up of many different variables and data inputs. These variables and data inputs behave in sometimes unpredictable ways which can not be predicted a-priori. It has therefore been suggested that agent-based simulations are used to better understand the dynamics of the electricity market. Agent-based models provide the opportunity to integrate machine learning and artificial intelligence to add intelligence, make better forecasts and control the power market in better and more efficient ways. In this systematic literature review, we review 55 papers published between 2016 and 2021 which focus on machine learning applied to agent-based electricity market models. We find that research clusters around popular topics, such as bidding strategies. However, there exists a long-tail of different research applications that could benefit from the high intensity research from the more investigated applications.
