## 1 Introduction
### Background of Thermal Models for HVAC System of Office Buildings
On the other hand, if the skin temperature is lower than ambient temperature, heat will be absorbed by the body so that the skin temperature increases [10]. Even if heat exchange also occurs due to the metabolic process of the human body, since occupants usually sit or walk without too much metabolic heat production in office buildings, whether the individual is thermally comfortable or not is more related to skin temperature. Motivated by thermoregulation theory, many researchers have proposed thermal models with the mean skin temperature or the most representative local skin temperature. [PERSON] et al. [11] used the mean skin temperature of 10 measurement locations, including forehead, chest, upper arm, back, abdomen, elbow, hand, thigh, calf and foot to evaluate thermal comfort for a person in a sleepingposture under steady thermal environment. The results turned out that the mean skin temperature at the three different thermal comfort levels was statistically significant. In terms of local skin temperature, [PERSON] [12] tested skin temperatures of 10 body locations to select the best location for thermal comfort inference, including forehead, upper arm, belly, wrist, hand, thigh, calf and foot. The results turned out that wrist was the most responsive area to infer thermal comfort with the lowest p-value. Moreover, [PERSON] et al. [13] established multiple linear regression models to estimate thermal sensations with variables based on skin temperature. As a result, the models using temperatures of the fingertips and wrist showed the highest accuracy. [PERSON] et al. [14] also proposed multiple linear regression models to estimate thermal sensation under various active states using wrist skin temperature-related variables. The study indicated skin temperature, its time differential and heart rate could be used for estimating individual thermal sensation. Besides regression, thermal sensation prediction can also be seen as a classification problem where various classification algorithms can be implemented. In order to investigate other statistical methods to establish thermal models besides regression, a recent research on thermal comfort inference utilized the wearable device of a pair of eyeglass with infrared (IR) arrays, and demonstrated that 82.8% of prediction accuracy for detection uncomfortable conditions was obtained with hidden Markov model (HMM) [15]. Moreover, [PERSON] et al. [16] used wearable devices like wearable fitness trackers to measure physical movements, sweat level and skin temperature so as to infer individual thermal comfort with machine learning algorithms including random forest (RF) and support vector machine (SVM). In addition, [PERSON] et al. [17] implemented SVM to predict thermal demands using skin temperature collected from various locations with wearable sensors. As a result, SVM classifiers with linear kernel were preferred to Gaussian kernel, which achieved over 90% accuracy. Based on the literature review, classification models have performed so well as regression models. However, instead of a 7-point thermal sensation scale, most of those models only predict thermal sensations with three classes, which are uncomfortably warm, comfortable and uncomfortably cool.