Predictive control of heating systems using IoT and predictive analytics

The article explores the principles and methods of predictive control for heating systems using Internet of Things (IoT) technologies and predictive analytics to optimize energy consumption and maintain indoor temperature stability. The proposed model is designed to control the heating process in buildings by incorporating heat loss dynamics and leveraging external temperature forecasting. IoT sensors and external data from the OpenWeatherMap cloud service gather real-time environmental and system data. Predictive algorithms process this data to proactively generate control signals that adjust the heating system. The model adapts traditional PI/PID controllers for discrete-mode operation, particularly for modern boiler systems. This adaptation ensures high-quality transitional responses, prevents temperature overshoots, and minimizes fluctuations, all while maintaining energy efficiency and comfort. The implementation of the model is demonstrated using MATLAB Simulink for simulation and validation. Node-RED is used for software integration, utilizing the Modbus TCP protocol to enable communication between sensors, the control system, and actuators. The architecture follows a distributed “server–actuator” design, allowing centralized data processing with direct interaction with IoT devices. This setup facilitates integration with external services such as weather forecasting platforms. Modelling results confirm that integrating forecasted external temperature changes reduces energy consumption while ensuring stable indoor temperatures. The methodology demonstrates significant potential for energy-efficient heating control, highlighting the advantages of IoT technologies in automating climate control. Future developments could extend these techniques to broader building automation and energy management applications.