Large Language Model-Based Interpretable Machine Learning Control in Building Energy Systems

Abstract: The potential of Machine Learning Control (MLC) in HVAC systems is hindered by its opaque nature and inference mechanisms, which is challenging for users and modelers to fully comprehend, ultimately leading to a lack of trust in MLC-based decision-making. To address this challenge, this paper investigates and explores Interpretable Machine Learning (IML), a branch of Machine Learning (ML) that enhances transparency and understanding of models and their inferences, to improve the credibility of MLC and its industrial application in HVAC systems. Specifically, we developed an innovative framework that combines the principles of Shapley values and the in-context learning feature of LLMs. While the Shapley values are instrumental in dissecting the contributions of various features in ML models, LLM provides an in-depth understanding of the non-data-driven or rule-based elements in MLC; combining them, LLM further packages these insights into a coherent, human-understandable narrative. The paper presents a case study to demonstrate the feasibility of the developed IML framework for model predictive control-based precooling under demand response events in a virtual testbed. The results indicate that the developed framework generates and explains the control signals in accordance with the rule-based rationale.

• The paper presents a novel framework that enhances the interpretability of machine learning control in building energy systems using SHAP values and LLMs.
• It employs a case study with a virtual testbed of a small office building to validate the approach for precooling during demand response events.
• The framework promotes increased trust and practical adoption of AI-based control systems, leading to improved energy efficiency and sustainability.

Enhancing Interpretability in Machine Learning Control for Building Energy Systems

Introduction to Interpretable Machine Learning and LLMs in Building Energy Management

The adoption of Machine Learning Control (MLC) techniques in Heating, Ventilation, and Air Conditioning (HVAC) systems represents a significant advancement in optimizing building operations, contributing to reduced energy consumption and emissions. However, the intrinsic complexity and opaqueness of MLC pose major challenges in their wider acceptance and application. Addressing these challenges, recent research led by Liang Zhang and Zhelun Chen introduces an innovative framework combining Shapley additive explanation (SHAP) values and LLMs to render MLC decisions in building energy systems more interpretable and transparent to users.

The Interpretable Machine Learning Framework

The paper presents a systematic approach that leverages the potential of SHAP values to quantify the contribution of various features in ML models and enhance their interpretability. SHAP values, rooted in cooperative game theory, offer a mathematically rigorous method for attributing prediction outcomes to individual features. Nevertheless, while SHAP values provide a significant advancement towards model interpretability, they do not fully address the gap in understanding complex control decisions in MLC, especially when such decisions are derived from the aggregation of multiple models and rules.

To bridge this gap, the researchers integrated LLMs into the interpretive process, utilizing their capacity to generate coherent narratives from structured data and explanations provided by SHAP. This integration allows for a more comprehensive understanding of MLC decisions by translating mathematical and model-specific explanations into intuitively accessible language, thus fostering trust and facilitating more informed decision-making among building operators.

Methodology and Case Study Implementation

The methodology section of the paper delineates the steps undertaken to combine SHAP values with LLMs. It starts with understanding the rule-based parts of Model Predictive Control (MPC), employing SHAP for generating interpretability results for each machine learning model involved, and culminates in utilizing LLMs to package these insights into a coherent narrative. This is followed by a case study where the proposed framework was applied to a virtual testbed featuring a small office building. The case study demonstrates the utility of the framework in providing interpretable control signals for precooling operations during demand response events.

Implications and Future Directions

This research holds significant implications for the practical application of MLC in building energy management. By enhancing the interpretability of MLC, the framework can play a crucial role in increasing the adoption and trust in AI-enhanced control systems among building operators. This, in turn, has the potential to facilitate more efficient energy use, reduce operational costs, and contribute to broader environmental sustainability goals.

Looking ahead, the paper identifies the need for further refinement of the integration between SHAP values and LLMs, particularly in automating and scaling the interpretive process to accommodate complex control scenarios. Additionally, future research could explore the application of this framework in other domains where MLC is employed, potentially extending its benefits beyond building energy systems.

Conclusion

In summary, the paper by Zhang and Chen introduces a pioneering approach to making machine learning control in building energy systems more interpretable and accessible. Through the innovative combination of SHAP values and LLMs, the proposed framework marks a significant step forward in bridging the trust gap between machine learning practitioners and building operators. As the field progresses, the continued development and application of such frameworks will be integral to harnessing the full potential of AI and ML in enhancing building energy efficiency and sustainability.