🤖 AI Summary
This study addresses the challenge of identifying abnormal operating conditions in district heating systems by proposing an integrated multivariate anomaly detection approach that combines principal component analysis (PCA), Isolation Forest, and Hotelling’s T² test. The method explicitly accounts for practical operational constraints—such as data invalidity during zero-energy-consumption periods—by incorporating outdoor temperature and heat transfer data. Detected anomalies are further refined through domain expert evaluation to ensure relevance and validity. Experimental results demonstrate that the proposed ensemble framework accurately identifies non-normal operating states, offering reliable detection performance while supporting operational decisions aimed at reducing natural gas consumption and carbon emissions.
📝 Abstract
In this paper, we test different methods for multivariate detection of outliers in the data of transmitted heat energy in the selected substation of local District Heating System, by also considering outside ambient temperature, namely Z-score (univariate, as a benchmark), Mahalanobis distances, Principal Component Analysis (PCA), Isolation Forest and Hotelling's T-squared test. The overall research aims at uncovering irregular plant operation, with a wider objective of identifying the opportunities for reducing the consumption of gas in central heating plants as well as the CO2 emission. The proposed approach considers specific domain circumstances, such as irrelevance of zero transmit-ted energy timepoints as indication of off-grid plant. The outcomes of the different methods are discussed with domain experts. It was concluded that PCA, Isolation Forest and Hotelling method provide relevant results. Finally, we adopt the ensemble method (selection based on the agreement of all three methods on the detected outliers) as the final approach.