在当今世界，由于快节奏的商业和旅游需求，频繁旅行已成为一种必要，酒店业的需求也在急剧上升。此外，客人对酒店大楼舒适住宿的要求也增加了，但保持热舒适条件会导致供暖、通风和空调系统的能耗大幅上升。然而，由于不同的日程安排和需求（商务、购物、观光等），房间占用时间的百分比较低。 )个人客人。由于舒适的住宿是头等大事，因此即使客房空无一人，也要通过保持暖通空调的运行来保持舒适的温度。然而，在客人不在时，可以通过关闭房间的HVAC来减少能耗，但这会导致客人回到客房时感到不舒服。因此，在酒店建筑中，在保持客人舒适的同时降低能耗是一个权衡问题。在本文中，我们通过求解交易提出了一种节能算法- 解决问题，即在保证客人舒适的同时以最佳方式降低能耗。客人的舒适度是由预测的不满意百分比10和5%的偏差来定义的，但很难保持最低的舒适度，因为热动力学受到各种建筑/暖通空调因素的影响。为了在舒适性保证的情况下优化节能，我们开发了两个人工神经网络模型来预测热动力学和能耗，并提出了一种算法来寻找最佳控制参数。利用试验台真实数据和参考气候下的EnergyPlus模型对我们提出的算法进行了评估，结果表明，在舒适性保证的情况下，节能率为32%-35%。 引文:2017年冬季会议，内华达州拉斯维加斯，会议论文

In today's world where frequent traveling has become a necessity due to the fast paced business and tour needs, the hospitality industry is also experiencing a sharp rise in demand. Moreover the guest requirements for comfortable stay at a hospitality building also increased, but maintaining thermal comfort condition causes a huge rise in energy consumption for Heating, ventilation and Air Conditioning system. However, the percentage of occupied duration in a room is low due to the varying schedule and needs (business, shopping, sightseeing etc.) of individual guest. Since the comfortable stay is the top most priority so comfort temperature is maintained by keeping the HVAC running even though guest rooms are empty. However, the energy consumption can be reduced during the guest's absence by turning the HVAC off for the room, but it causes dis-comfort of the guest on coming back to the guest room. So it poses the trade-off problem to maintain the comfort for the guest and reduce the energy consumption at the same time in a hospitality building.In this paper, we proposed an energy saving algorithm by solving the trade-off problems, i.e., reduce the energy consumption optimally while guarantee the guest comfort. Guest comfort is defined by Predicted Percentage Dissatisfied 10 with 5% deviation but it is hard to maintain the minimal comfort because thermal dynamics is influenced by diverse building/HVAC factors. To optimize energy saving with comfort guarantee, we develop two Artificial Neural Network models to predict the thermal dynamics and the energy consumption and propose an algorithm to find the optimal control parameter. Our proposed algorithm is evaluated by using both the test bed real data and the EnergyPlus model with reference climates, and results show 32%~35% energy saving rate with comfort guarantee.