论文标题
集中式软演员评论家深入强化学习方法通过城市Lealen对地区需求方面的管理
A Centralised Soft Actor Critic Deep Reinforcement Learning Approach to District Demand Side Management through CityLearn
论文作者
论文摘要
强化学习是在地区一级的未来智能电网的一部分的需求侧管理的有前途的无模型和自适应控制器。本文介绍了为城市挑战提交的算法的结果，该算法于2020年初举办，目的是设计和调整强化学习剂，以平坦并平滑不同建筑区的电气需求的总曲线。拟议的解决方案使用集中的“软演员评论家”深入强化学习者在挑战中获得了第二名，该技术能够处理连续的动作空间。控制器能够在包括不同建筑物和气候的挑战数据集上达到0.967的平均得分。这凸显了深度加固学习作为插件风格的控制器的潜在应用，该控制器能够处理不同的气候和异质建筑物的库存，用于建筑物的地区需求侧管理。
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.
