AI-driven optimization: revolutionizing energy efficiency in modern buildings.

Area of Science:

• Building energy management
• Artificial intelligence in smart buildings
• Control theory and optimization

Background:

• Growing global energy demand and decarbonization goals necessitate advanced building energy management systems.
• Conventional control strategies face challenges with temporal continuity, adaptability, and performance in diverse building environments.
• Existing methods struggle with nonlinear dynamics and multi-objective operational trade-offs.

Purpose of the Study:

• To introduce NODE-RL-BEM, a unified continuous-time optimization paradigm for intelligent building energy management.
• To jointly model building system dynamics and learn adaptive control policies using integrated data.
• To address limitations of conventional discrete-time and simulation-dependent control strategies.

Main Methods:

• Developed NODE-RL-BEM, integrating heterogeneous operational data, temporal state embeddings, and neural ordinary differential equation modeling.
• Employed multi-objective reinforcement learning within a cohesive architecture for predictive and responsive energy optimization.
• Utilized continuous-time dynamics learning for improved predictive fidelity and smooth state evolution.

Main Results:

• Achieved 42-48% energy savings and maintained comfort violations below 0.5%.
• Improved indoor air quality by 28-35% across diverse datasets.
• Demonstrated strong transferability and stability with a generalization score of 0.91.

Conclusions:

• NODE-RL-BEM offers a novel continuous-time dynamic-policy learning paradigm for sustainable and autonomous building energy management.
• The framework effectively integrates predictive modeling with real-time adaptive control for dynamic environments.
• Scalable applicability to multi-zone buildings confirms practical deployment feasibility.