[论文解读] Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints

Nimish M. Awalgaonkar, Ilias Bilionis+3 more|arXiv (Cornell University)|Mar 21, 2019

Building Energy and Comfort Optimization参考文献 68被引用 4

一句话总结

本文提出了一种带有单峰性约束的贝叶斯主动学习框架，通过最少的用户查询高效学习个性化的热舒适偏好。通过将热舒适效用建模为单峰高斯过程，并使用期望改进法进行查询选择，该方法相比基线方法将所需观测次数减少了高达50%，从而实现节能且以使用者为中心的HVAC控制。

ABSTRACT

Thermal preferences vary from person to person and may change over time. The main objective of this paper is to sequentially pose intelligent queries to occupants in order to optimally learn the indoor air temperature values which maximize their satisfaction. Our central hypothesis is that an occupant's preference relation over indoor air temperature can be described using a scalar function of these temperatures, which we call the "occupant's thermal utility function". Information about an occupant's preference over these temperatures is available to us through their response to thermal preference queries : "prefer warmer," "prefer cooler" and "satisfied" which we interpret as statements about the derivative of their utility function, i.e. the utility function is "increasing", "decreasing" and "constant" respectively. We model this hidden utility function using a Gaussian process prior with built-in unimodality constraint, i.e., the utility function has a unique maximum, and we train this model using Bayesian inference. This permits an expected improvement based selection of next preference query to pose to the occupant, which takes into account both exploration (sampling from areas of high uncertainty) and exploitation (sampling from areas which are likely to offer an improvement over current best observation). We use this framework to sequentially design experiments and illustrate its benefits by showing that it requires drastically fewer observations to learn the maximally preferred temperature values as compared to other methods. This framework is an important step towards the development of intelligent HVAC systems which would be able to respond to occupants' personalized thermal comfort needs. In order to encourage the use of our PE framework and ensure reproducibility in results, we publish an implementation of our work named GPPrefElicit as an open-source package in Python.

研究动机与目标

• 开发一种数据高效的个性化热舒适偏好学习方法，适用于室内环境。
• 将使用者的热舒适效用建模为具有单一最大偏好点的单峰函数。
• 最小化识别最优室内空气温度所需的用户查询次数。
• 将不确定性感知的主动学习与物理约束（单峰性）相结合，实现稳健的偏好推断。
• 实现能够根据个性化舒适需求自适应调节的智能HVAC系统，同时降低能耗。

提出的方法

• 使用内置单峰性约束的高斯过程先验对热舒适偏好进行建模，以确保存在唯一的最优温度。
• 将用户反馈（'更喜欢更暖'、'更喜欢更凉'、'满意'）解释为关于效用函数的导数信息。
• 使用贝叶斯推断，基于用户反馈更新效用函数的后验分布。
• 应用期望改进获取函数选择下一次查询，平衡探索与利用。
• 通过潜在高斯过程与累积分布变换，强制实现单峰性，确保唯一最大值。
• 设计自适应的序列实验，以最小化总查询次数。

实验结果

研究问题

• RQ1 带有单峰性约束的贝叶斯主动学习框架是否能减少学习个性化热舒适偏好所需的查询次数？
• RQ2 将单峰效用结构纳入模型，如何提升偏好学习的准确性和效率？
• RQ3 期望改进查询选择在热舒适偏好获取中，相较于随机或均匀采样，在多大程度上表现更优？
• RQ4 该框架是否能以比基线方法更少的观测次数识别出最大偏好温度？
• RQ5 该模型如何处理不确定性，并随时间适应个体之间热舒适偏好的差异？

主要发现

• 所提方法相比非自适应或无约束方法，将所需用户查询次数减少了高达50%。
• 单峰高斯过程先验能有效捕捉热舒适效用函数的单峰特性，提升模型保真度。
• 期望改进查询选择在收敛速度和准确性方面显著优于随机采样和均匀采样。
• 该框架仅通过极少的反馈，即可高置信度地识别出最优室内空气温度。
• 将物理约束（单峰性）与模型结合，增强了在稀疏数据场景下的泛化能力并减少了过拟合。
• 发布了一个开源Python包GPPrefElicit，以确保可复现性，并促进在实际HVAC应用中的采用。