A Comfort-Based Approach to Smart Heating and Air Conditioning
A Comfort-Based Approach to Smart Heating and Air Conditioning
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DOI：
10.1145/3057730
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发表时间：
2018-02-01
影响因子：
5
通讯作者：
Rogers, Alex
中科院分区：
文献类型：
--
作者：
Auffenberg, Frederik;Snow, Stephen;Rogers, Alex
在本文中，我们将解决预测用户舒适度的相关挑战，并利用这些挑战来降低智能供暖、通风和空调（HVAC）系统的能耗。目前，这种系统在决定设定点温度时使用简单的用户舒适度模型。这些模型是使用广泛的人口统计数据建立的，通常不能代表个人用户的偏好，导致对用户偏好温度的估计很差。为了解决这个问题，我们提出了贝叶斯舒适度模型（Bayesian Comfort Model，简写为EEE）。这种个性化的热舒适度模型使用贝叶斯网络从用户的反馈中学习，使其能够随着时间的推移适应用户的个人偏好。我们进一步提出了一种替代ASHRAE 7点量表用于评估用户舒适度。使用这个模型，我们创建了一个最佳的HVAC控制算法，最大限度地减少能源消耗，同时保持用户的舒适度。通过基于ASHRAE RP-884数据集和我们在单独部署中收集的数据的实证评估，我们表明我们的模型始终比当前模型准确13.2%至25.8%，以及如何使用我们的替代舒适度可以提高我们模型的准确性。通过仿真，我们表明，使用该模型，我们的暖通空调控制算法可以减少7.3%至13.5%的能源消耗，同时减少用户的不适24.8%。
In this article, we address the interrelated challenges of predicting user comfort and using this to reduce energy consumption in smart heating, ventilation, and air conditioning (HVAC) systems. At present, such systems use simple models of user comfort when deciding on a set-point temperature. Being built using broad population statistics, these models generally fail to represent individual users' preferences, resulting in poor estimates of the users' preferred temperatures. To address this issue, we propose the Bayesian Comfort Model (BCM). This personalised thermal comfort model uses a Bayesian network to learn from a user's feedback, allowing it to adapt to the users' individual preferences over time. We further propose an alternative to the ASHRAE 7-point scale used to assess user comfort. Using this model, we create an optimal HVAC control algorithm that minimizes energy consumption while preserving user comfort. Through an empirical evaluation based on the ASHRAE RP-884 dataset and data collected in a separate deployment by us, we show that our model is consistently 13.2% to 25.8% more accurate than current models and how using our alternative comfort scale can increase our model's accuracy. Through simulations we show that using this model, our HVAC control algorithm can reduce energy consumption by 7.3% to 13.5% while decreasing user discomfort by 24.8% simultaneously.