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This preprint compares XGBoost and LSTM for forecasting transmitted heat energy in District Heating Systems, finding that XGBoost consistently outperforms LSTM while offering lower computational cost and environmental impact.
This paper evaluates different multivariate outlier detection methods (Z-score, Mahalanobis distances, PCA, Isolation Forest, and Hotelling's T-squared) for identifying irregular operations in district heating system data, proposing an ensemble approach based on agreement among the best-performing methods.