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Data-Driven Anomaly Detection in Energy Consumption Using Deep Learning and Adaptive Threshold Calibration
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Resumo(s)
Campus energy consumption monitoring at scale is challenging because university buildings are heterogeneous and loads vary with weather, occupancy schedules, and seasonal transitions. Heating was selected as the focus because it is a major and strongly weather-driven contributor to campus demand. In addition, operational datasets rarely contain labelled fault events, limiting supervised evaluation of anomaly detection methods. This thesis proposed a forecast-driven anomaly screening framework for campus heating-energy monitoring using 15-minute smart-meter data from ten buildings at the University of Münster. A joint multi-building GRU model, conditioned on building identity, learns normal heating demand dynamics from cumulative heat meter readings transformed into 15-minute incremental consumption (kWh) and provides one-step-ahead forecasts as an expected baseline. Candidate anomalies are defined as persistent deviations between measured consumption and this baseline and are detected using residual scoring with adaptive EWMA thresholds to account for non-stationary residual behaviour. To enable calibration and benchmarking without ground truth, event-based synthetic anomaly injection is applied to derive per-building detector parameters and evaluate sensitivity across fault-like patterns. The GRU achieved R² = 0.86 and WAPE = 0.24 on the test split. In the injected-event evaluation, the detector achieved high precision; recall was highest for sustained positive deviations (over-delivery and bias), moderate for drift, and lowest for under-delivery and freeze-like patterns that resemble legitimate low-load operation. Finally, the framework was applied on an independent deployment-style monitoring window (October–December 2025), where detected event candidates were aggregated per building and visualized in a prototype campus spatial dashboard to support cross-building comparison and operational prioritization.
Descrição
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
Palavras-chave
Campus heating monitoring Gated Recurrent Units forecasting Adaptive thresholding Synthetic fault injection