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.

翻译：机器学习控制（MLC）在暖通空调系统中的潜力因其不透明性和推理机制而受到制约，用户和建模者难以完全理解，最终导致对基于MLC的决策缺乏信任。为解决这一挑战，本文研究并探索了可解释机器学习（IML）——机器学习（ML）的一个分支，旨在增强模型及其推理的透明度和可理解性——以提升MLC的可信度及其在暖通空调系统中的工业应用。具体而言，我们开发了一个创新框架，将沙普利值的原理与大语言模型（LLMs）的上下文学习特性相结合。沙普利值有助于剖析ML模型中各种特征的贡献，而LLM则提供了对MLC中基于规则部分的深入理解；两者结合，LLM进一步将这些见解整合成连贯且易于人类理解的叙述。本文通过一个案例研究，在虚拟测试平台中展示了所开发IML框架在需求响应事件下基于模型预测控制的预冷操作中的可行性。结果表明，该框架能够根据基于规则的原理生成并解释控制信号。