HVAC systems occupy a large part of the building’s energy consumption. The development of fault detection and diagnosis (FDD) techniques for HVAC system is becoming increasingly essential for building energy saving. The present paper proposes a comparison study on the basic data-driven methods for variable refrigerant flow (VRF) system fault diagnosis. And five widely used data-driven methods were analyzed, which were decision tree (DT), support vector machines (SVM), clustering (CL), shallow neural networks (SNN), and deep neural networks (DNN). The six common types of fault data in VRF system and three evaluation indexes were used to compare the performance of the proposed five FDD methods in single fault and multiple faults. Results indicate that the single fault diagnosis performance of all methods is better than multiple fault diagnosis. The performances of DNN, SNN, and SVM are better than CART and CL no matter single fault or multiple fault diagnosis. Among them, we believe that the SVM method is preferred for single fault diagnosis and the DNN method is preferred for multiple fault diagnosis. And the performance of method CL is the worst, especially in the case of multiple fault classification, the accuracy is only about 48%. The study results are dedicated to providing a reference for subsequent research in FDD of VRF system. Some important remarks are finally concluded in this paper.

暖通空调系统占据了建筑能耗的很大一部分。暖通空调系统故障检测与诊断（FDD）技术的开发对于建筑节能变得越来越重要。本文对可变制冷剂流量（VRF）系统故障诊断的基本数据驱动方法进行了比较研究。分析了五种广泛使用的数据驱动方法，分别是决策树（DT），支持向量机（SVM），聚类（CL），浅层神经网络（SNN）和深层神经网络（DNN）。使用VRF系统中的六种常见故障数据和三个评估指标来比较所提出的五种FDD方法在单故障和多故障中的性能。结果表明，所有方法的单一故障诊断性能均优于多重故障诊断。无论是单故障还是多故障诊断，DNN，SNN和SVM的性能均优于CART和CL。其中，我们相信SVM方法是首选的单故障诊断方法，而DNN方法是首选的多故障诊断方法。方法CL的性能最差，特别是在多重故障分类的情况下，准确性仅为48％左右。研究结果致力于为VRF系统FDD的后续研究提供参考。本文最后总结了一些重要的观点。方法CL的性能最差，特别是在多重故障分类的情况下，准确性仅为48％左右。研究结果致力于为VRF系统FDD的后续研究提供参考。本文最后总结了一些重要的观点。方法CL的性能最差，特别是在多重故障分类的情况下，准确性仅为48％左右。研究结果致力于为VRF系统FDD的后续研究提供参考。本文最后总结了一些重要的观点。