In order to improve the operation of district-heating systems, it is necessary for the energy companies to have reliable optimization routines, both computerized and manual, implemented in their organizations. However, before a production plan for the heat-producing units can be constructed, a prediction of the heat demand first needs to be determined. The outdoor temperature, together with the social behavior of the consumers, have the greatest influence on the demand. This is also the core of the load prediction model developed in this paper. Several methodologies have been proposed for heat-load forecasting, but due to lack in measured data and due to the uncertainties that are present in the weather forecasts, many of them will fail in practice. In such situations, a simpler model may give as good predictions as an advanced one. This is also the experience from the applications analyzed in this paper.