Deep Learning-Based Distributed Optimization Method for Building Energy Management

Buildings consume approximately 40% of the world’s energy, and managing their energy usage is crucial for sustainability. This article presents a theoretical analysis of a deep learning-based approach for efficient building energy management. The proposed method uses distributed optimization to ensure that each zone in the building consumes only its fair share of the available power, while satisfying user comfort requirements.
Imagine a large apartment building with multiple rooms, each with its own thermostat. The challenge is to balance the heating and cooling needs of each room while ensuring that the total energy consumption does not exceed the available power. Traditional methods rely on a centralized controller that must juggle the temperature settings for each room to meet the collective demand. However, this approach can lead to inefficiencies and waste, especially when dealing with multiple zones.
The proposed deep learning-based approach addresses these issues by distributing the optimization process across all zones. Each zone has its own local objective function, which is combined with a global objective to ensure that the total power consumption remains within bounds. The algorithm uses a sharing problem formulation, similar to dividing a pizza among friends, where each zone receives an appropriate share of the energy based on its needs and constraints.
The key insight is that the coordinator (i.e., the central controller) chooses the optimal amount of power for each room and timestep to ensure that the power constraint is satisfied. The local controllers (i.e., the thermostats in each room) then adjust the temperature setpoints to track this power target while preserving user comfort. By decentralizing the optimization process, the algorithm can handle complex building structures and non-linear constraints more effectively.
The article provides a theoretical analysis of the proposed approach, demonstrating its efficiency and scalability through numerical experiments. The results show that the deep learning-based method outperforms traditional centralized control methods in terms of energy savings and user satisfaction. Moreover, the algorithm is flexible enough to handle various building types and insulation configurations, making it a promising solution for large commercial buildings and beyond.
In summary, this article presents a deep learning-based approach for efficient building energy management that leverages distributed optimization to balance energy consumption across multiple zones. By decentralizing the control process, the algorithm can handle complex building structures and non-linear constraints more effectively, resulting in significant energy savings and improved user comfort.