  This paper introduces a Generative Adversarial Nets (GAN) based, Load Profile
Inpainting Network (Load-PIN) for restoring missing load data segments and
estimating the baseline for a demand response event. The inputs are time series
load data before and after the inpainting period together with explanatory
variables (e.g., weather data). We propose a Generator structure consisting of
a coarse network and a fine-tuning network. The coarse network provides an
initial estimation of the data segment in the inpainting period. The
fine-tuning network consists of self-attention blocks and gated convolution
layers for adjusting the initial estimations. Loss functions are specially
designed for the fine-tuning and the discriminator networks to enhance both the
point-to-point accuracy and realisticness of the results. We test the Load-PIN
on three real-world data sets for two applications: patching missing data and
deriving baselines of conservation voltage reduction (CVR) events. We benchmark
the performance of Load-PIN with five existing deep-learning methods. Our
simulation results show that, compared with the state-of-the-art methods,
Load-PIN can handle varying-length missing data events and achieve 15-30%
accuracy improvement.
