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European countries are increasingly capturing waste heat from crematoria to warm homes, swimming pools, and public buildings, turning an otherwise wasted byproduct into a low-carbon energy source that reduces fuel costs.
This study systematically evaluates TabPFN-TS for zero-shot probabilistic heat load forecasting in district heating networks, comparing it with state-of-the-art time-series foundation models like Chronos-2 and machine-learning baselines.
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.