To face the challenges of climate change, the integration of renewable energy sources is a crucial aspect of emission reduction in the energy-intensive heating sector. For an efficient operation of coupling devices such as heat pumps with intermittent sources of renewable energy, accurate heat load forecasts need to be developed and embedded into an operation strategy to enable further decarbonisation of heat generation. Data analysis driven forecasts based on weather data hold the potential of identifying consumption patterns to forecast day-ahead heat demand and have been studied extensively for electricity demand forecasts. However, it remains to be shown how such forecasts can be applied in an actual heating system. In this study, we propose a control strategy that utilizes hourly heat load forecasts with a 24-hours rolling horizon. First, we investigate supervised and unsupervised forecasting techniques with regard to accuracy and computational efficiency based on three different heat load data sets. The results are mixed but the application of convolutional neural networks on data of the district heating network in Flensburg, Germany is the most promising outcome. Elaborating further on this example, we then develop a control strategy and demonstrate how a heat load forecast can be used to improve the utilization of offshore wind generation or reduce energy costs through a heat pump and a heat storage system. Thus, we contribute to the electrification of the heat sector and thereby enable a reduction of carbon emissions.