This article considers testing for mean-level shifts in functional data. The class of the famous Darling-Erdős-type cumulative sums (CUSUM) procedures is extended to functional time series under short range dependence conditions which are satisfied by functional analogues of many popular time series models including the linear functional AR and the non-linear functional ARCH. We follow a data driven, projection-based approach where the lower-dimensional subspace is determined by (long run) functional principal components which are eigenfunctions of the long run covariance operator. This second-order structure is generally unknown and estimation is crucial - it plays an even more important role than in the classical univariate setup because it generates the finite-dimensional subspaces. We discuss suitable estimates and demonstrate empirically that altogether this change-point procedure performs well under moderate temporal dependence. Moreover, Darling-Erdős-type change-point estimates based on (long run) functional principal components as well as the corresponding "fully-functional" counterparts are provided and the testing procedure is finally applied to publicly accessible electricity data from a German power company.