Abstract
This study is based on the system architecture of the Internet of Things (IoT) Smart House, and presents an application system that uses fuzzy control to efficiently control load devices, in order to provide thermal comfort for indoor environments. This system adopts the scatter layout method to determine the best indoor node for measurement, assesses stability through minimum variation, and reliability through the minimum mean deviation, and uses a questionnaire to discuss whether the experimental data are different from human feelings. In this study, the thermal comfort index is calculated according to the ISO 7730 standard, and two methods, including the Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD), are adopted to assess the human perception of thermal comfort in the whole space. There are six kinds of data for assessment, which fall into the categories of environmental factors and personal factors. The data of the environmental factor are air temperature, mean radiant temperature, relative humidity, and air velocity, and the data of the personal factor are clothing insulation, and metabolic.
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