Reinforcement Learning (RL) Meets Urban Climate Modeling: Investigating the Efficacy and Impacts of RL-Based HVAC Control (2505.07045v1)

Abstract: Reinforcement learning (RL)-based heating, ventilation, and air conditioning (HVAC) control has emerged as a promising technology for reducing building energy consumption while maintaining indoor thermal comfort. However, the efficacy of such strategies is influenced by the background climate and their implementation may potentially alter both the indoor climate and local urban climate. This study proposes an integrated framework combining RL with an urban climate model that incorporates a building energy model, aiming to evaluate the efficacy of RL-based HVAC control across different background climates, impacts of RL strategies on indoor climate and local urban climate, and the transferability of RL strategies across cities. Our findings reveal that the reward (defined as a weighted combination of energy consumption and thermal comfort) and the impacts of RL strategies on indoor climate and local urban climate exhibit marked variability across cities with different background climates. The sensitivity of reward weights and the transferability of RL strategies are also strongly influenced by the background climate. Cities in hot climates tend to achieve higher rewards across most reward weight configurations that balance energy consumption and thermal comfort, and those cities with more varying atmospheric temperatures demonstrate greater RL strategy transferability. These findings underscore the importance of thoroughly evaluating RL-based HVAC control strategies in diverse climatic contexts. This study also provides a new insight that city-to-city learning will potentially aid the deployment of RL-based HVAC control.

Reinforcement Learning for HVAC Control and Urban Climate Modelling

The paper "Reinforcement Learning (RL) Meets Urban Climate Modeling: Investigating the Efficacy and Impacts of RL-Based HVAC Control" presents an integrated framework that leverages reinforcement learning in concert with sophisticated urban climate models. The primary objective is to explore the applicability and impact of RL-based strategies for HVAC (heating, ventilation, and air conditioning) control across varied climatic zones. It notably incorporates a building energy model (BEM) within an urban climate context to provide a nuanced assessment of HVAC systems.

Context and Methodology

With urbanization contributing significantly to the urban heat island (UHI) effect, efficient HVAC systems have a pivotal role in managing indoor thermal comfort and optimizing energy consumption. Existing studies highlight the role anthropogenic heat plays in exacerbating UHI, emphasizing the pressing need to refine HVAC strategies particularly amid climate change.

The authors employ the Community Land Model Urban (CLMU), a component of the Community Earth System Model (CESM), which allows for comprehensive simulation of urban energy dynamics. This framework integrates a BEM designed to model heat and energy flows within buildings, taking into account the bidirectional interactions between the indoor environment and the surrounding urban atmosphere.

The paper utilizes multiple reinforcement learning methodologies, including deep Q-learning (DQN) and soft actor-critic (SAC), to optimize HVAC control. These RL algorithms are implemented within a Fortran-based climate model, effectively using Python to port BEM components to develop a training environment conducive to RL.

Results

The paper reveals that SAC achieves superior performance among the RL algorithms tested, balancing thermal comfort with energy efficiency more effectively than heuristic methods. The experiments demonstrate that RL-based control strategies are particularly advantageous in warmer climates due to a higher frequency of HVAC operation. Cities with seasonal temperature variability like Beijing exhibit enhanced adaptability to RL strategies.

The authors identify an interesting trend: the benefits obtained from RL are contingent on both geographical and seasonal factors, underscoring the sensitivity of RL rewards to different climatic contexts. For example, RL techniques increased building temperatures during colder months while reducing them in warmer periods across different cities, illustrating the need for climate-specific control strategies.

Practical Implications and Transferability

The ability to transfer RL strategies across different cities was evaluated, wherein models trained in climates with substantial temperature variability showed superior cross-application efficacy. This finding suggests that RL strategies developed in diverse climatic conditions may offer more robust solutions when applied to novel environments.

Conclusion and Future Directions

While this paper makes significant strides in demonstrating the potential of RL-infused HVAC systems, it also delineates areas for future exploration. The existing BEM simplifies some building parameters, and future work could benefit from incorporating more detailed simulations. Furthermore, deploying fully coupled global climate models would deepen our understanding of large-scale impacts, including global climate feedbacks from widespread HVAC systems.

This research marks a notable advancement in utilizing reinforcement learning to enhance urban climate modeling, with promising implications for energy management and environmental sustainability on a global scale. The results underline the critical need for integrating climate-specific information into RL systems to achieve optimal HVAC performance in diverse urban landscapes.