  The integration of the global Photovoltaic (PV) market with real time
data-loggers has enabled large scale PV data analytical pipelines for power
forecasting and long-term reliability assessment of PV fleets. Nevertheless,
the performance of PV data analysis heavily depends on the quality of PV
timeseries data. This paper proposes a novel Spatio-Temporal Denoising Graph
Autoencoder (STD-GAE) framework to impute missing PV Power Data. STD-GAE
exploits temporal correlation, spatial coherence, and value dependencies from
domain knowledge to recover missing data. Experimental results show that
STD-GAE can achieve a gain of 43.14% in imputation accuracy and remains less
sensitive to missing rate, different seasons, and missing scenarios, compared
with state-of-the-art data imputation methods such as MIDA and LRTC-TNN.
