使用深度强化学习的室温和双向EV充电的数据驱动控制：模拟和实验
Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: simulations and experiments
摘要：
这项工作提出了一个完全数据驱动的，黑箱管道，以获得一个最优控制政策的多回路建筑控制问题的基础上，历史建筑和天气数据，从而无需复杂的基于物理的建模。我们展示了联合控制室温和双向电动汽车充电的方法，以最大限度地提高乘员的热舒适性和节能，同时在电动汽车电池中留下足够的能量用于下一次旅行。我们用递归神经网络模拟室温，用分段线性函数模拟电动汽车充电。使用这些模型作为仿真环境，我们应用深度强化学习（DRL）算法来获得最优控制策略。学习的政策实现了平均17%的能源节约超过供暖季节和19%的舒适度比标准的RB室温控制器的满意度。当额外连接双向EV并应用双费率电价时，MIMO DRL策略成功地利用了电池，并与两个标准RB控制器（一个控制室温，另一个控制双向EV（放电）充电）相比降低了总电力成本。最后，我们展示了一个成功的转移学习DRL政策从模拟到一个真实的建筑，DFAB房子在瑞士Empa Duebendorf，实现了高达30%的节能，同时保持类似的舒适度水平相比，传统的RB室温控制器在三个星期的供暖季节。
英文摘要：
This work presents a fully data-driven, black-box pipeline to obtain an optimal control policy for a multi-loop building control problem based on historical building and weather data, thus without the need for complex physics-based modelling. We demonstrate the method for joint control of room temperature and bidirectional EV charging to maximize the occupant thermal comfort and energy savings while leaving enough energy in the EV battery for the next trip. We modelled the room temperature with a recurrent neural network and EV charging with a piece-wise linear function. Using these models as a simulation environment, we applied a deep reinforcement learning (DRL) algorithm to obtain an optimal control policy. The learnt policy achieves on average 17% energy savings over the heating season and 19% better comfort satisfaction than a standard RB room temperature controller. When a bidirectional EV is additionally connected and a two-tariff electricity pricing is applied, the MIMO DRL policy successfully leverages the battery and decreases the overall cost of electricity compared to two standard RB controllers, one controlling the room temperature and another controlling the bidirectional EV (dis-)charging. Finally, we demonstrate a successful transfer of the learnt DRL policy from simulation onto a real building, the DFAB HOUSE at Empa Duebendorf in Switzerland, achieving up to 30% energy savings while maintaining similar comfort levels compared to a conventional RB room temperature controller over three weeks during the heating season.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
