This paper analyzes the distributional impacts of customer diversity on hierarchical load forecasting. Existing research focuses on the group size of the hierarchy but ignores customer diversity within groups such as technologies, tariffs and behaviours. Given the same group size, customer diversity has distributional impacts on forecasting accuracy. For example, higher penetration of photovoltaics could bring more uncertainties while homogeneous customers might reinforce their seasonality, which would widen the spread of errors. This paper utilizes PV and residential datasets from different areas and tariffs to create a pool of diverse profiles. Subsets are sampled and forecasted to study the relationship between customer diversity and probability distributions of forecasting errors. Results indicate that technology brings the greatest impact by altering the mean absolute percentage error maximally by 50.38%, followed by lifestyle (13.60%) and tariffs (5.76%). The overall impacts of customer diversity gradually fade out as the aggregation level (i.e. group size) increases.