Intelligent Tuning of PID Using Metaheuristic Optimization for Temperature and Relative Humidity Control of Comfortable Rooms In order to provide the comfortable areas for human, the comfortable rooms are the basic needs area, controlled temperature, and relative humidity (RH). The aim of this study is to control and maintain the temperature and RH of the comfortable room using a proportional integral derivative (PID) controller tuned by metaheuristic optimization. In tuning gains of the PID controller, the modern metaheuristic optimizations, ant colony optimization (ACO), and symbiotic organism search (SOS) are applied and the performance of the proposed control system is compared to that of the traditional methods. In the experimental testing, the controlled room size is tested in the area of width 7.80xe2x80x89m, length 8.00xe2x80x89m, and height 3.80xe2x80x89m. The simulation results show that the performance of the proposed control systemtuned gains of PID controllers by using SOS algorithm has the least steadystate error with 15% rise time and also the overshoot can reach the setpoint. In the case of disturbance occurring in the system, the proposed control system is able to approach the setpoint. Therefore, the PID controller tuned by SOS algorithm can regulate the temperature and humidity of the comfortable room, proficiently.

Intelligent Thermal Comfort Controlling System for Buildings Based on IoT and AI With the improvement of technologies, peopleu0027s demand for intelligent devices of indoor and outdoor living environments keeps increasing. However, the traditional control system only adjusts living parameters mechanically, which cannot better meet the requirements of human comfort intelligently. This article proposes a building intelligent thermal comfort control system based on the Internet of Things and intelligent artificial intelligence. Through the literature review, various algorithms and prediction methods are analyzed and compared. The system can automatically complete a series of operations through IoT hardware devices which are located at multiple locations in the building with key modules. The code is developed and debugged by Python to establish a model for energy consumption prediction with environmental factors such as temperature, humidity, radiant temperature, and air velocity on thermal comfort indicators. By using the simulation experiments, 1700 data sets are used for training. Then, the output PMV predicted values are compared with the real figure. The results show that the performance of this system is superior to traditional control on energysaving and comfort.