Multiagent Systems; Reactive agents; Intelligent systems; Automatization; Home Assistant; ESP32; Node-RED

This study presents the design, implementation, and evaluation of a multi-agent system (MAS) for intelligent heating control in residential buildings. The system integrates three reactive agents, manual, seasonal and neural, operat-ing within a Home Assistant platform to optimize thermal comfort while mini-mizing energy consumption. Through the combination of rule-based control, sea-sonal patterns, and machine learning predictions, the proposed approach demon-strates significant improvements over conventional thermostat systems. Experi-mental validation using ESP32 microcontrollers, wireless sensors, and Node-RED automation shows promising results for energy efficiency and adaptive control. The neural agent, trained on historical data using a multilayer perceptron (MLP) classifier, achieves optimal performance with ReLU activation functions, demon-strating the potential for intelligent and user-adaptive heating systems in smart home environments.

This study presents the design, implementation, and evaluation of a multi-agent system (MAS) for intelligent heating control in residential buildings. The system integrates three reactive agents, manual, seasonal and neural, operat-ing within a Home Assistant platform to optimize thermal comfort while mini-mizing energy consumption. Through the combination of rule-based control, sea-sonal patterns, and machine learning predictions, the proposed approach demon-strates significant improvements over conventional thermostat systems. Experi-mental validation using ESP32 microcontrollers, wireless sensors, and Node-RED automation shows promising results for energy efficiency and adaptive control. The neural agent, trained on historical data using a multilayer perceptron (MLP) classifier, achieves optimal performance with ReLU activation functions, demon-strating the potential for intelligent and user-adaptive heating systems in smart home environments.