The rapid and uncertain penetration of distributed energy resources is changing the way people are consuming electricity. This development increases the risk of instability and congestion in the grid, posing great challenges to system operators in the near future. In order to optimally allocate resources and plan grid enhancement, distribution system operators need new tools to monitor the local energy transition and assess its future impact. Smart meter data is an important resource in gaining this insight, but unlocking the full potential requires new analytical methodologies. Recent research focused on identifying typical daily energy consumption patterns - load shapes - but a clear interpretation of these results is lacking. This research proposes a latent lifestyle model, which models energy consumption as a mixture of different lifestyles, each of which is defined by a distribution over load shapes. This extra layer of abstraction proves to be an effective way of identifying lifestyle-like patterns, that allow for a clear interpretation. Consumers can then be grouped based on their mixture of inferred lifestyles, serving as an input for grid simulation. Such a simulation showed that the all-electric homes cause the aggregated load to nearly triple compared to conventional consumers, due to their increased peak demand and simultaneity.