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 Large
Language Models (LLMs). While the Shapley values are instrumental in dissecting
the contributions of various features in ML models, LLM provides an in-depth
understanding of rule-based parts 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.

通过结合 Shapley 值和大型语言模型（LLM）的特点，本文研究了可解释的机器学习（IML）在 HVAC 系统中的应用，以提高 MLC 的可信度和工业应用。作者通过案例研究在虚拟试验平台上展示了开发的 IML 框架在需求响应事件中的模型预测控制上的可行性，并证明该框架按照基于规则的原理生成和解释控制信号。