Dynamic Line Rating (DLR) is a promising field of research aiming to help network operators face challenges, such as increased penetration of renewable energies and peak electricity demand. Research on realtime overhead line ampacity estimation is currently advanced and, in the last few years, research has started to address medium-term DLR forecasting. The focus is on probabilistic forecasts, in order to select ratings associated with very low probability of occurrence. For this reason, 1%-quantiles are usually selected. In this paper, the authors propose a methodology for selecting the most appropriate quantiles based on a cost-benefit analysis, considering both the economic benefits of an increased line ampacity and the costs associated with a DLR forecast that is higher than its observed value. The proposed methodology is evaluated using realistic weather data on a virtual line connecting Belgium and France.