One of the requirements for the transition from conventional district heating (DH) systems to 4th generation DH (4GDH) systems is the knowledge of system dynamics. Forecast of thermal request profiles of buildings is crucial to optimize the operating conditions. In fact, when this is available, the thermal load evolution at the plants can be estimated and proper energy saving actions can be implemented. In this paper, a smart and fast approach for estimating the daily thermal request of buildings in large networks is presented. The methodology uses only data available from the smart meters installed in the building substations (mass flow and temperature data). For the users where smart meter data are not available an alternative approach is proposed. The methodology is shown to be suitable for applications involving a) a very large number of buildings b) necessity of forecast of an area c) measured data, which might be affected by gaps d) low computational time requirements. Experimental data show that, despite the simplicity, the method predicts the thermal request very accurately. Furthermore, the forecasted thermal request are here effectively used with the aim of reducing the peak load in one of the largest DH systems in Europe. (C) 2019 Elsevier Ltd. All rights reserved.