The paper introduces the Explainable AI methodology to assess the global feature importance of the Machine Learning models used for heat demand forecasting in intelligent control of District Heating Systems (DHS), with motivation to facilitate their interpretability and trustworthiness, hence addressin g the challenges related to adherence to communal standards, customer satisfaction and liability risks. Methodology involves generation of global feature importance insights by using four different approaches, namely intrinsic (ante-hoc) interpretability of Gradient Boosting method and selected post-hoc methods, namely Partial Dependence, Accumulated Local Effects (ALE) and SHAP and qualitative analysis of those insights in context of expected behavior of DHS and comparative analysis. None of the selected methods assume feature permutation or perturbations which can introduce bias due to introduction of random unrealistic values of data instances. ALE and SHAP have been found as most reliable methods for determining the feature importance, taking into account feature interactions and nonlinearities. ALE plots with transmitted energy across the range of ambient temperatures closely resemble the shape of the control curve, which is the evidence of accurate model, as well as suitability of explanation method. By providing the insights which align with the domain expertise, the discussion confirms the value of using Explainable AI stack as mandatory layer in assessing the performance of ML models, especially in high-risk AI systems, such as those whose use is anticipated in the DHS.