Adaptive Energy Optimization

Improved building energy efficiency using adaptive multi-agent reinforcement learning control strategies.

Key Takeaways:

Static control policies limited efficiency gains

Rule-based energy optimization struggled to adapt to dynamic occupancy, environmental conditions, and system interactions across complex building environments.

We reframed energy optimization as a learning system

Energy control was redesigned as a continuous learning problem, allowing policies to improve through interaction rather than manual tuning.

Multi-agent reinforcement learning for distributed control

Independent agents were trained using reinforcement learning with graph-based state representations. Simulation environments modeled coordination across interconnected systems to enable scalable control strategies.

32% reduction in energy consumption

Adaptive control delivered sustained efficiency improvements across real-world building environments.