Reinforcement Learning-Based Management in IoT-Enabled Renewable Energy Communities: An Approach to Optimization for...

Area of Science:

• Energy Systems
• Artificial Intelligence
• Smart Grids

Background:

• The rise of Internet of Things (IoT) and distributed renewable energy necessitates advanced energy management in Renewable Energy Communities (RECs).
• Decentralized, intelligent, and adaptive strategies are crucial for optimizing energy within RECs.

Purpose of the Study:

• To propose a sensor-driven reinforcement learning (RL) framework for coordinated energy management in residential RECs.
• To jointly optimize thermal comfort, economic savings, and environmental sustainability within these communities.

Main Methods:

• A Q-learning agent controls heating and appliances using IoT sensor data (temperature, energy, presence).
• A stochastic simulation environment models weather, building dynamics, user behavior, and solar generation.
• A two-stage RL training strategy pre-trains agents individually before community-level deployment with shared rewards.

Main Results:

• The RL framework significantly outperforms rule-based control in energy consumption, thermal comfort, and overall reward.
• Pre-trained agents exhibit stable, cooperative behavior at the community level, demonstrating robustness to exploration.
• The approach proves viable and scalable for decentralized energy management in IoT-enabled RECs.

Conclusions:

• Sensor-driven, lightweight reinforcement learning offers an effective solution for managing decentralized energy resources in smart communities.
• The proposed framework successfully balances individual household needs with community-wide energy optimization goals.
• This research paves the way for more efficient and sustainable energy management in the era of smart homes and RECs.