Attributing localized US electricity demand anomalies to major events (after normalizing for time and weather).
This research project and paper were completed for a graduate course in distributed data processing with Spark/Hadoop/Python.
A novel methodology was developed to identify localized spatio-temporal events in which electricity demand from the grid decouples from its primary dependent factors, time (periodicity) and weather. The decoupling events are aligned (in space and time) with news media event data recorded in the Global Database of Events, Language, and Tone (GDELT), in an attempt to gain insight about significant events that influence power consumption in the US.