Smart thermostats are one of the most prevalent home automation products. Despite the importance of having an accurate thermal model for the operation of smart thermostats, reliable identification of this model is still an open problem. In this paper, we explore various techniques for establishing a suitable thermal model using time series data generated by smart thermostats. We show that Bayesian neural networks can be used to estimate parameters of a grey-box thermal model if sufficient training data is available, and this model outperforms several black-box models in terms of the temperature prediction accuracy. Leveraging real data from 8,884 homes equipped with smart thermostats, we discuss how the prior knowledge about the model parameters can be utilized to quickly build an accurate thermal model for another home with similar floor area and age in the same climate zone. Moreover, we investigate how to adapt the model originally built for the same home in another season using a small amount of data collected in this season. Our results confirm that maintaining only a small number of pre-trained thermal models will suffice to quickly build accurate thermal models for many other homes, and that 1 day smart thermostat data could significantly improve the accuracy of transferred models in another season.

智能恒温器是最流行的家庭自动化产品之一。尽管对于智能恒温器的操作而言，具有精确的热模型很重要，但是可靠地识别该模型仍然是一个未解决的问题。在本文中，我们探索了使用智能恒温器生成的时间序列数据建立合适的热模型的各种技术。我们显示，如果有足够的训练数据，则可以使用贝叶斯神经网络来估计灰箱热模型的参数，并且就温度预测准确性而言，该模型优于几种黑箱模型。利用来自8884个配备智能恒温器的房屋的真实数据，我们将讨论如何利用有关模型参数的先验知识，为同一气候区域内具有相似建筑面积和年龄的另一套房屋快速建立准确的热模型。此外，我们使用本季节收集到的少量数据，研究如何改编最初在另一个季节为同一房屋建造的模型。我们的结果证实，仅维护少量的经过预先训练的热模型就足以为许多其他房屋快速建立准确的热模型，并且1天的智能恒温器数据可以显着提高另一个季节中转移的模型的准确性。