DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of
  EV Charging Load
Siyang Li Hui Xiong Yize Chen
Due to the vast electric vehicle (EV) penetration to distribution grid,
charging load forecasting is essential to promote charging station operation
and demand-side management.However, the stochastic charging behaviors and
associated exogenous factors render future charging load patterns quite
volatile and hard to predict. Accordingly, we devise a novel Diffusion model
termed DiffPLF for Probabilistic Load Forecasting of EV charging, which can
explicitly approximate the predictive load distribution conditioned on
historical data and related covariates. Specifically, we leverage a denoising
diffusion model, which can progressively convert the Gaussian prior to real
time-series data by learning a reversal of the diffusion process. Besides, we
couple such diffusion model with a cross-attention-based conditioning mechanism
to execute conditional generation for possible charging demand profiles. We
also propose a task-informed fine-tuning technique to better adapt DiffPLF to
the probabilistic time-series forecasting task and acquire more accurate and
reliable predicted intervals. Finally, we conduct multiple experiments to
validate the superiority of DiffPLF to predict complex temporal patterns of
erratic charging load and carry out controllable generation based on certain
covariate. Results demonstrate that we can attain a notable rise of 39.58% and
49.87% on MAE and CRPS respectively compared to the conventional method.