Granular, multi-year time series of building-level electricity consumption are fundamental to distribution planning processes to support the adoption of solar panels, battery storage, and electric vehicles. These data are often measured using smart meters. However, some electric utilities do not have the hardware or data transmission processes in place to collect this key dataset. The absence of time series data can slow the adoption of distributed energy resources by making it difficult to prepare for their impacts on distribution grid infrastructure. This session covers Kevala’s load simulation tool, a modeling system used to synthesize granular electricity consumption time series data at the building level. The load simulation tool relies on static features, such as parcel attributes, and time-varying features, such as weather, to simulate hourly demand values over arbitrary time spans. Kevala’s load simulation tool learns relationships between these features and the scale and temporal variability of measured energy consumption values from smart meter readings. The results of Kevala’s load simulation tool have been used as a basis for modeling how the proliferation of solar panels, battery storage, and electric vehicles impacts electricity consumption.