Detecting deviations in building time series data is essential for robust heating, ventilation, and air conditioning (HVAC) operation and energy-efficient facility management. In practice, however, building management system (BMS) data are often incomplete, heterogeneous, and lack reliable fault labels.This paper presents a benchmarking and feasibility study of data-driven anomaly detection on multivariate air-handling unit (AHU) time series data under realistic deployment constraints. We construct a unified dataset and define a domain-informed rule-based baseline as an interpretable operational reference and source of weak labels. We further evaluate classical unsupervised methods and representation-learning approaches using Temporal Convolutional Network (TCN) and Time Series Mixer (TSMixer) autoencoders, considering both a joint multivariate representation of all selected sensors and subsystem-based representations in which sensors are grouped by AHU function. Additionally, SHapley Additive exPlanations-based (SHAP) attribution is used to improve interpretability by identifying the sensor-level contributions to detected deviations.The results show that rule-based methods capture explicitly defined conditions, while data-driven approaches identify additional statistically unusual and temporally structured deviations, with representation-learning models flagging 1.1–1.4% of windows in the global setting and up to 4.7% in subsystem-based analyses. High-consensus events (~0.8%) occur during temporally localized episodes with agreement across multiple models, indicating robust, structured deviations. These detections represent candidate anomalies that require further validation. Our results show that combining rule-based, classical, and representation-learning methods provides complementary insights into AHU behavior and helps screen for relevant deviations in performance and energy use.
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