We present the dataset containing time-series open circuit output voltage traces of indoor photovoltaic cell corresponding to occupant door crossing events to perform smart home occupant identification. We collect shadow patterns of five participants from two different doors in two rooms of a building. We collect a total of 900 door entry and exit events during different hours of the day. We sample the voltage at 50 hz and provide the raw timestamped data. We also pre-process the data to filter the event of interest and label the data with occupant id and type of door events. We provide two example scripts to demonstrate how to process raw data and apply machine learning models for occupant identification using solar cell voltage samples.