Divide-Conquer Transformer Learning for Predicting Electric Vehicle
  Charging Events Using Smart Meter Data
Fucai Ke Hao Wang
Predicting electric vehicle (EV) charging events is crucial for load
scheduling and energy management, promoting seamless transportation
electrification and decarbonization. While prior studies have focused on EV
charging demand prediction, primarily for public charging stations using
historical charging data, home charging prediction is equally essential.
However, existing prediction methods may not be suitable due to the
unavailability of or limited access to home charging data. To address this
research gap, inspired by the concept of non-intrusive load monitoring (NILM),
we develop a home charging prediction method using historical smart meter data.
Different from NILM detecting EV charging that has already occurred, our method
provides predictive information of future EV charging occurrences, thus
enhancing its utility for charging management. Specifically, our method,
leverages a self-attention mechanism-based transformer model, employing a
``divide-conquer'' strategy, to process historical meter data to effectively
and learn EV charging representation for charging occurrence prediction. Our
method enables prediction at one-minute interval hour-ahead. Experimental
results demonstrate the effectiveness of our method, achieving consistently
high accuracy of over 96.81\% across different prediction time spans. Notably,
our method achieves high prediction performance solely using smart meter data,
making it a practical and suitable solution for grid operators.