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A Centralised Soft Actor Critic Deep Reinforcement Learning Approach to District Demand Side Management through CityLearn
arXiv - CS - Machine Learning Pub Date : 2020-09-22 , DOI: arxiv-2009.10562
Anjukan Kathirgamanathan, Kacper Twardowski, Eleni Mangina, Donal Finn

Reinforcement learning is a promising model-free and adaptive controller for demand side management, as part of the future smart grid, at the district level. This paper presents the results of the algorithm that was submitted for the CityLearn Challenge, which was hosted in early 2020 with the aim of designing and tuning a reinforcement learning agent to flatten and smooth the aggregated curve of electrical demand of a district of diverse buildings. The proposed solution secured second place in the challenge using a centralised 'Soft Actor Critic' deep reinforcement learning agent that was able to handle continuous action spaces. The controller was able to achieve an averaged score of 0.967 on the challenge dataset comprising of different buildings and climates. This highlights the potential application of deep reinforcement learning as a plug-and-play style controller, that is capable of handling different climates and a heterogenous building stock, for district demand side management of buildings.

中文翻译:

通过 CityLearn 进行区域需求侧管理的集中式 Soft Actor Critic 深度强化学习方法

强化学习是一种有前途的无模型和自适应控制器,用于需求侧管理,作为未来智能电网的一部分,在地区层面。本文介绍了为 2020 年初举办的 CityLearn 挑战赛提交的算法的结果,该挑战赛旨在设计和调整强化学习代理,以压平和平滑不同建筑物区域的电力需求汇总曲线。所提出的解决方案使用能够处理连续动作空间的集中式“Soft Actor Critic”深度强化学习代理,在挑战中获得第二名。在包含不同建筑物和气候的挑战数据集上,控制器能够获得 0.967 的平均分数。
更新日期:2020-09-23
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