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DATA DRIVEN ANOMALY DETECTION IN HEAT PUMPS
Abstract
The decarbonization of the building sector and the recent disruption of energy markets has positioned Air Source Heat Pumps (ASHP) as a pivotal technology for reducing fossil fuel dependency and improving energy efficiency. However, their widespread deployment introduces challenges related to operational reliability, seasonal performance variability, and early fault detection. This study presents a data-driven approach for performance analysis and anomaly detection in real-world ASHP systems. Using five years of monitoring data collected via the HeatPumpMonitor platform (OpenEnergyMonitor), operational parameters such as thermal output, electrical consumption, flow/return/outside/room temperatures were analyzed and validated against GeoTSOL simulations to ensure data integrity. Multiple statistical anomaly detection techniques, including Z-score, Interquartile Range (IQR) and Median Absolute Deviation (MAD) were applied to determine deviations from nominal behavior. In parallel, a Long Short-Term Memory Autoencoder (LSTM-AE) was implemented to model temporal dependencies and detect operational anomalies through reconstruction error analysis. The proposed real-time data pipeline integrates data acquisition, data processing, unsupervised training, and inference, enabling continuous monitoring and visualization of heat pump performance. Results demonstrate that combining statistical and deep learning techniques enhances fault sensitivity and robustness, enabling intelligent maintenance strategies that improve system efficiency and reliability.
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