Monday 09 June 2025
As cities continue to grow and urban populations expand, understanding how to efficiently cool buildings has become a pressing issue. One solution is to harness the power of reinforcement learning (RL) to optimize heating, ventilation, and air conditioning (HVAC) systems in buildings. A recent study explored the potential of RL-based HVAC control strategies across different cities with varying climates.
The researchers used a combination of climate models and building energy management systems (BEMS) to simulate the performance of various RL algorithms in five cities: London, New York, Beijing, Hong Kong, and Singapore. They found that each city presented unique challenges for the RL algorithms, which had to adapt to local temperature profiles, humidity levels, and building characteristics.
The study focused on three popular RL algorithms: Q-learning, deep Q-networks (DQN), and soft actor-critic (SAC). Each algorithm was trained using a combination of simulated data from the cities’ climate models and BEMS. The results showed that all three algorithms were able to learn and adapt to the local conditions in each city.
One key finding was that the performance of the RL algorithms varied significantly depending on the city’s climate. In hot and humid cities like Hong Kong, the DQN algorithm outperformed the others, while in cooler climates like London, the Q-learning algorithm performed better. The SAC algorithm showed consistent performance across all five cities.
The researchers also explored the transferability of the models between cities. They found that while the RL algorithms were able to adapt to local conditions in each city, they struggled to generalize to other cities with different climate profiles. This suggests that RL-based HVAC control strategies may need to be tailored specifically to a particular city’s climate and building characteristics.
The study also highlighted the importance of considering humidity levels when designing HVAC systems. In cities like Hong Kong, which experiences high levels of relative humidity, the researchers found that neglecting humidity in the design process could lead to reduced system performance.
Overall, this study demonstrates the potential of RL-based HVAC control strategies for optimizing building energy efficiency and comfort. As cities continue to grow and urban populations expand, developing tailored solutions for specific climates and building characteristics will be crucial for ensuring a sustainable future.
Cite this article: “Reinforcement Learning-Based HVAC Control Strategies for Efficient Building Cooling Across Diverse Cities”, The Science Archive, 2025.
Reinforcement Learning, Hvac Systems, Building Energy Efficiency, Urban Planning, Climate Modeling, Deep Q-Networks, Soft Actor-Critic, Q-Learning, Transfer Learning, Energy Management Systems
