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

TL;DR

This paper introduces an interpretable machine learning framework combining Shapley values and large language models to enhance transparency and trust in model predictive control for HVAC systems, demonstrated through a case study.

Contribution

It develops a novel IML framework that integrates Shapley values and LLMs to improve interpretability of MLC in building energy systems.

Findings

Framework effectively explains control signals with rule-based rationale

Demonstrates feasibility in virtual testbed for demand response

Enhances trust and understanding of MLC in HVAC applications

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 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 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.