  To adequately constrain the frequency of energy deposition in active region
cores in the solar corona, systematic comparisons between detailed models and
observational data are needed. In this paper, we describe a pipeline for
forward modeling active region emission using magnetic field extrapolations and
field-aligned hydrodynamic models. We use this pipeline to predict
time-dependent emission from active region NOAA 1158 as observed by SDO/AIA for
low-, intermediate-, and high-frequency nanoflares. In each pixel of our
predicted multi-wavelength, time-dependent images, we compute two commonly-used
diagnostics: the emission measure slope and the time lag. We find that
signatures of the heating frequency persist in both of these diagnostics. In
particular, our results show that the distribution of emission measure slopes
narrows and the mean decreases with decreasing heating frequency and that the
range of emission measure slopes is consistent with past observational and
modeling work. Furthermore, we find that the time lag becomes increasingly
spatially coherent with decreasing heating frequency while the distribution of
time lags across the whole active region becomes more broad with increasing
heating frequency. In a follow up paper, we train a random forest classifier on
these predicted diagnostics and use this model to classify real AIA
observations of NOAA 1158 in terms of the underlying heating frequency.
