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Standalone off-grid electrical systems, no matter where they are deployed or for what user class, are designed based upon the load they are expected to serve. State-of-the-art computerized off-grid system design tools require the user to specify the expected load profile, that is, how the power consumption changes throughout the day. Often, this is at an hourly resolution, and some characterization of the distribution of power around the average values may be required. Specifying realistic and reasonable load profiles is a barrier to the appropriate design of standalone systems. This research extends previous studies on daily energy consumption of residential solarpowered off-grid systems on the Navajo Nation to provide hourly load profiles, statistical characteristics, and probabilistic models. The data analyzed come from 90 homes over a two-year period. K-means clustering is used to identify prototypical normalized load profiles when the data are grouped by year, season, weekday, and weekend. Eight parametric probability density functions are fit to the grouped data at an hourly resolution. Their fit to the data is evaluated using the Cram´er-von Mises (CvM) statistic. The results show that the load profiles tend to be night-peaking and that Log Normal and Gumbel distributions can reasonably model variation in the data. The load profiles and probabilistic models can be used in off-grid design software and to synthesize load profiles for design and future research.