Abstract 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.

中文翻译：

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智能多区住宅暖通空调控制的多任务深度强化学习

摘要 在这个简短的交流中，应用数据驱动的深度强化学习（deep RL）方法来最小化暖通空调用户的能源消耗成本，同时保持用户的舒适度。应用的深度强化学习方法的效率通过进行多任务学习来提高，该学习可以在冷却和加热场景中为多区域住宅 HVAC 系统实现经济控制策略。将应用的多任务深度强化学习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，以验证其在优化 HVAC 操作中的有效和普遍应用。