This paper concerns the feasibility study of 7 classes of thermal sensation detection in Indonesia's indoor environment using a low-cost thermal camera through face skin temperature. This study is required as an initial step to build a thermal comfort sensor system of HVAC control systems to produce a comfortable indoor environment with minimum and efficient energy use. The feasibility study was started by studying the thermoregulation system of respondents in Indonesia through measuring their body and facial skin temperatures under heating and cooling conditions, including their relationship with thermal sensations. The facial skin temperature variable, which is covered by four measurement points, namely forehead, nose, cheeks, and chin, represents the MST variable by the coefficient of determination of 0.54. The thermal sensation detection algorithm based on Artificial Neural Network (ANN) is 35.7% of accuracy. The thermal sensation questionnaire with 7 class categories is unsuitable for Indonesian respondents, and the number of the category classes predicted too much compared to the number of inputs. The detection algorithm has better accuracy with a smaller number of classes, namely 52.2% and 68.70% for the 5 and 3 classes of thermal sensation.

Practical application: The air conditioning buildings system is possible to influence a thermal environmental control system to meet the occupants' thermal comfort level requirement in an indoor environment if the system is equipped with a sensor that can detect the occupants' thermal sensations. The thermal camera can be used as a non-contact sensor, detecting the occupant’s thermal sensation by reading the occupant's face skin temperature in an indoor environment.

本文涉及使用低成本热像仪通过面部皮肤温度在印度尼西亚室内环境中进行7种热感检测的可行性研究。这项研究是构建HVAC控制系统的热舒适传感器系统的第一步，需要以最少且有效的能源使用产生舒适的室内环境。通过研究印度尼西亚受访者的体温调节系统来开始可行性研究，方法是测量他们在加热和冷却条件下的身体和面部皮肤温度，包括它们与热感的关系。脸部皮肤温度变量（由额头，鼻子，脸颊和下巴四个测量点覆盖）通过确定系数0.54表示MST变量。基于人工神经网络（ANN）的热感检测算法的准确度为35.7％。具有7个类别类别的热感问卷不适用于印度尼西亚受访者，与输入的数量相比，类别类别的数量预测的太多。该检测算法具有较少类别的更好的精度，即对于5和3个类别的热感，分别为52.2％和68.70％。

实际应用：如果空调建筑系统装有可检测居住者热感的传感器，则它可能会影响热环境控制系统以满足居住者在室内环境中的热舒适水平要求。热像仪可用作非接触式传感器，通过读取室内环境中乘员的面部皮肤温度来检测乘员的热感觉。