Multi-task deep reinforcement learning for intelligent multi-zone residential HVAC control
Multi-task deep reinforcement learning for intelligent multi-zone residential HVAC control
复制标题
DOI：
10.1016/j.epsr.2020.106959
复制
发表时间：
2020-11
影响因子：
3.9
通讯作者：
Yan Du;F. Li;J. Munk;Kuldeep R. Kurte;O. Kotevska;Kadir Amasyali;H. Zandi
中科院分区：
文献类型：
--
作者：
Yan Du;F. Li;J. Munk;Kuldeep R. Kurte;O. Kotevska;Kadir Amasyali;H. Zandi
在这一短暂的交流中，应用了一种数据驱动的深度强化学习方法来最小化暖通空调用户的能耗成本，同时保持用户的舒适性。通过进行多任务学习，提高了深度学习方法的效率，实现了多区域住宅暖通空调系统在供冷和供暖两种情况下的经济控制策略。应用多任务深度RL方法与基于规则的基准案例和单任务深度确定性策略梯度算法进行比较，验证了该方法在暖通空调优化运行中的有效性和普适性。
In this short communication, a data-driven deep reinforcement learning (deep RL) method is applied to minimize HVAC users’ energy consumption costs while maintaining users’ comfort. The applied deep RL method's efficiency is enhanced by conducting multi-task learning that can achieve an economic control strategy for a multi-zone residential HVAC system in both cooling and heating scenarios. The applied multi-task deep RL method is compared with a rule-based benchmark case and a single-task deep deterministic policy gradient algorithm to verify its effective and generalized application in optimizing HVAC operation.