Temperature and Humidity Control System with Air Conditioner Based on Fuzzy Logic and Internet of Things

Abstract

Work is an activity that takes most of the day to earn a living and improve the standard of living. During work, many people have to work indoors, which can be a less comfortable and unhealthy place if the temperature and humidity are not well controlled. Unsuitable temperature and humidity conditions can negatively affect the health and comfort of workers, as well as interfere with productivity and work quality. However, the problem that often arises is the difficulty of controlling room temperature and humidity effectively, especially in rooms that are closed and do not get air circulation from outside. Therefore, an effective solution is needed to control the temperature and humidity of the room automatically and remotely via the internet. The contribution of this research is to develop an effective and efficient AC control system in controlling room temperature and humidity using Tsukamoto's Fuzzy Inference System (FIS) method and the Internet of Things (IoT). Tsukamoto's FIS is used to produce AC temperature values in room temperature and humidity control as measured by the DHT22 sensor directly integrated with the ESP32 microcontroller. This control system is monitored remotely using IoT concepts through a mobile application interface. The results of this study show that room temperature can be controlled under normal conditions, with an average change of -1.67°C and an overall average temperature of 25.95°C. While the average humidity is at a value of 80.16% which is included in the Wet set. This suggests that humidity cannot be controlled under normal conditions, so it still requires further development. In addition, it is also necessary to further investigate the effectiveness of the tool in various sizes and more complex layouts of rooms.

Energy Efficiency with Internet of Things Based Fuzzy Inference System for Room Temperature and Humidity Regulation

“…This research represents a continuation of prior studies on temperature and humidity control systems utilizing FIS and IoT-based AC (20). However, this study specifically concentrates on devising an effective and efficient AC control system for regulating room temperature and humidity using Tsukamoto's FIS and IoT methods, without evaluating the efficiency of energy consumption, both in the short-term and long-term usage (20). Particularly, it addresses the utilization of long-term AC systems, which are categorized as inefficient (21)(22)(23).…”

“…The Fuzzy Tsukamoto model is a fuzzy logic-based control approach developed in past decades (33,34). This approach combines the principles of fuzzy logic with intuitively defined stages that were previously conducted [20], and (b) the more advanced stages implemented in this study linguistic rules to control complex and ambiguous systems. Fuzzy Tsukamoto utilizes the concept of fuzzy membership to depict uncertainty in system inputs and outputs.…”

“…This objective directs the transition towards wiser, more sustainable, and more impactful energy utilization for individuals. This research represents a continuation of prior studies on temperature and humidity control systems utilizing FIS and IoT-based AC (20). However, this study specifically concentrates on devising an effective and efficient AC control system for regulating room temperature and humidity using Tsukamoto's FIS and IoT methods, without evaluating the efficiency of energy consumption, both in the short-term and long-term usage (20).…”

Fuzzy logic method-based stress detector with blood pressure and body temperature parameters

“…The membership function of body temperature is divided into four levels: cool body temperature, slightly cold, slightly hot, and hot body temperature, with a range of less than 34 °C to more than 35 °C. The mathematical model of body temperature membership function [27] is annotated with ( 5)- (8). Graphic of body temperature membership function can be seen in Figure 3.…”

“…DASS 42 is a questionnaire with 42 questions, consisting of 3 emotional scales: depression, anxiety, and stress with levels as shown in Table 5; normal, mild, moderate, severe, and very severe [31]. The DASS 42 scale can be classified into 3 [32], which are the depression scale (questions number 3,5,10,13,16,17,21,24,26,31,34,37,38,42), the anxiety scale (questions number 2, 4, 7, 9, 15, 19, 20, 23, 25, 28, 30, 36, 40, 41.3), and the stress scale (questions number 1,6,8,11,12,14,18,22,27,29,32,33,35,39).…”