Temperature Simulation Using Nonlinear Autoregressive with Exogenous Algorithm Integrated with PID Controller

In fields such as process control, heating, ventilation, air conditioning (HVAC) systems, and industrial automation, temperature simulation is a crucial component. Conventional temperature management techniques, such as proportional, integral and derivative (PID) controllers frequently have trouble capturing the nonlinear dynamics of temperature systems. In this study, a method that combines a PID controller with a Nonlinear Autoregressive with Exogenous (NARX) algorithm to increase the accuracy of temperature simulation is developed and evaluated. Indoor and outdoor temperature data is collected from Malaysian Ministry of Investment, Trade, and Industry (MITI) building to create a thorough dataset for the model. The results show temperature simulation accuracy and control performance of PID controller systems. Setpoint tracking from the integrated system successfully captures nonlinear dynamics and adapts to various dynamic temperature patterns. The developed system offers a viable way to improve temperature simulation and control. The challenge of accurately simulating temperature dynamics with conventional PID controllers is satisfactorily addressed in this study. The Integral of Time Multiply Squared Error (ITSE) approach is used to evaluate the controller performances by calculating the error between the obtained graph line and the desired temperature setting (Tset). The outcome is displayed as an index value. The study's findings offer important information for the creation and use of sophisticated temperature simulation approaches in diverse industrial and automation contexts.