Maintaining a pleasant indoor environment with low energy consumption is important for healthy and comfortable living in buildings. In previous studies, we proposed the integrated comfort control (ICC) algorithm, which integrates several indoor environmental control devices, including an air conditioner, a ventilation system, and a humidifier. The ICC algorithm is operated by simple on/off control to maintain indoor temperature and relative humidity within a defined comfort range. This simple control method can cause inefficient building operation because it does not reflect the changes in indoor–outdoor environmental conditions and the status of the control devices. To overcome this limitation, we suggest the artificial intelligence integrated comfort control (AI2CC) algorithm using a double deep Q-network(DDQN), which uses a data-driven approach to find the optimal control of several environmental control devices to maintain thermal comfort with low energy consumption. The suggested AI2CC showed a good ability to learn how to operate devices optimally to improve indoor thermal comfort while reducing energy consumption. Compared to the previous approach (ICC), the AI2CC reduced energy consumption by 14.8%, increased the comfort ratio by 6.4%, and decreased the time to reach the comfort zone by 54.1 min.

中文翻译：

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使用双深度Q网络对室内环境设备进行热舒适性的新型集成和最优控制

维持低能耗的宜人室内环境对于建筑物健康舒适的生活至关重要。在以前的研究中，我们提出了集成舒适控制（ICC）算法，该算法集成了几种室内环境控制设备，包括空调，通风系统和加湿器。ICC算法通过简单的开/关控制进行操作，以将室内温度和相对湿度保持在定义的舒适范围内。这种简单的控制方法无法反映室内外环境条件和控制设备状态的变化，因此可能导致建筑物运行效率低下。为克服此限制，我们建议使用双深度Q网络（DDQN）的人工智能集成舒适度控制（AI2CC）算法，它使用数据驱动的方法来找到多个环境控制设备的最佳控制，以保持低能耗的热舒适性。建议的AI2CC具有良好的学习能力，可以学习如何最佳地操作设备以提高室内热舒适度，同时降低能耗。与以前的方法（ICC）相比，AI2CC减少了14.8％的能耗，舒适度提高了6.4％，到达舒适区的时间减少了54.1分钟。