The application of thermal comfort control based on Smart House System of IoT

•This paper uses the scatter layout method to determine the best indoor node position.•The approach presents an application that uses fuzzy to efficiently control devices.•Employing system is based on the system architecture of the Internet of Things.•Establishing a comfortable environment is the mo...

Full description

Saved in:
Bibliographic Details
Published in:Measurement : journal of the International Measurement Confederation 2020-01, Vol.149, p.106997, Article 106997
Main Authors: Sung, Wen-Tsai, Hsiao, Sung-Jung
Format: Article
Language:English
Subjects:
Air temperature
Computer architecture
Control systems
Fuzzy control
Fuzzy systems
Indoor environments
Insulation
Internet of Things
IoT
Mathematical analysis
Relative humidity
Reliability analysis
Smart House
Smart houses
Stability analysis
Temperature
Thermal comfort
Thermal comfort index
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•This paper uses the scatter layout method to determine the best indoor node position.•The approach presents an application that uses fuzzy to efficiently control devices.•Employing system is based on the system architecture of the Internet of Things.•Establishing a comfortable environment is the most important goal of this research. 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.
ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2019.106997