Data efficient indoor thermal comfort prediction using instance based transfer learning method

Abstract

The precise prediction of indoor thermal sensation is critical to building energy efficiency and satisfaction of occupants. In this field, data-driven models have shown superior performance than Predicted Mean Vote (PMV) model. However, a reliable and accurate model of this kind relies on sufficient and high-quality data from physiological and environmental sensing, which in practice is difficult or expensive to collect. Rather than developing a separated predictive model for certain task, transfer learning method could intelligently use knowledge from similar domains to solve the target problem, and thus the requirement of data resources is reduced. In the last few years, transfer learning has been introduced into the field of thermal comfort prediction. Most of them used the source domain data as a whole for model pre-training, and the samples' suitability judgment was not fully considered. But the general situation in this field is: there are few public and available data resources and their data distributions are hardly consistent with that of specific target task. Aiming at the issue, this study proposes an instance based transfer learning model, which is based on data weighting and reusing for data efficient thermal comfort prediction. We assume that samples from source and target domains obey different distributions but have some common features that can be transferred across domains. The Nearest Neighbor Search (NNS) is used for similarity judgement and selection of source domain data; A Boosting type transfer learning algorithm, i.e., TrAdaBoost, is improved for reliable thermal comfort prediction. In the algorithm, an automatic weighting mechanism could optimally adjust the source training data's weights for performance improvement. By using field experimental data set and a public data set as target and source domains data respectively, the performance of the proposed model is investigated. Results show when target domain data is insufficient, the proposed NNS-iTrAdaBoost method has superior transfer learning ability and could achieve much better performance than traditional data-driven models. In practice, the proposed method is easy for engineering implementation and has great potential for thermal comfort prediction based on small-scale experiments.