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Large language model-based interpretable machine learning control in building energy systems
Journal article   Peer reviewed

Large language model-based interpretable machine learning control in building energy systems

Liang Zhang and Zhelun Chen
Energy and buildings, v 313, 114278
15 Jun 2024
DOI: https://doi.org/10.1016/j.enbuild.2024.114278
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

Building control Interpretable machine learning Large language model Machine learning control Shapley value
•Innovative interpretable machine learning framework for machine learning control.•Shapley values and large language models are combined for improved interpretability.•Case study demonstrates interpretable control processes in demand response events.•Bridging trust gap in machine learning control usage for building energy management. 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.

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70 citations in Web of Science

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#7 Affordable and Clean Energy
#11 Sustainable Cities and Communities
#13 Climate Action

Source: SDGs in the Output

InCites Highlights

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Collaboration types
Domestic collaboration
Web of Science research areas
Construction & Building Technology
Energy & Fuels
Engineering, Civil
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