Predictive Optimal Control Mechanism of Indoor Temperature Using Modbus TCP and Deep Reinforcement Learning

This research study proposes an indoor temperature regulation predictive optimal control system that entails the use of both deep reinforcement learning and the Modbus TCP communication protocol. The designed architecture comprises distributed sub-parts, namely, distributed room-level units as well as a centralized main-part AI controller for maximizing efficient HVAC management in single-family residences as well as small-sized buildings. The system utilizes an LSTM model for forecasting temperature trends as well as an optimized control action using an envisaged DQN with predicted states, sensors, as well as user preferences. InfluxDB is utilized for gathering real-time environmental data such as temperature and humidity, as well as consumed power, and storing it. The AI controller processes these data to infer control commands for energy efficiency as well as thermal comfort. Experimentation on an NVIDIA Jetson Orin Nano as well as on a Raspberry Pi 4 proved the efficacy of the system, utilizing 8761 data points gathered hourly over 2023 in Cheonan, Korea. An added hysteresis-based mechanism for controlling power was incorporated to limit device wear resulting from repeated switching. Results indicate that the AI-based control system closely maintains target temperature setpoints with negligible deviations, affirming that it is a scalable, cost-efficient solution for intelligent climate management in buildings.