5 citations · 8 across the 3 of their papers we have counts for
3 papers
cs.LG2026★ 2 cited
On the global feature importance for interpretable and trustworthy heat demand forecasting
Milan Zdravković
The paper introduces the ante-hoc Explainable AI methodology to assess the global feature importance of the Machine Learning models used for heat demand forecasting in intelligent…
cs.LG2026★ 5 cited
XGBoost "is all you need": the case of forecasting transmitted heat energy in District Heating Systems
Milan Zdravković
This paper presents a comparative study of two distinct approaches, XGBoost and Long-Short Term Memory (LSTM), for forecasting transmitted heat energy in District Heating Systems (…
cs.LG2026★ 1 cited
Towards an approach to multivariate outlier detection for District Heating System data
Rajko Turudija, Dušan Stojiljković, Milan Zdravković +1
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,…