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.
Cite
@article{arxiv.2302.10860,
title = {Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Photovoltaic Timeseries Data Imputation},
author = {Yangxin Fan and Xuanji Yu and Raymond Wieser and David Meakin and Avishai Shaton and Jean-Nicolas Jaubert and Robert Flottemesch and Michael Howell and Jennifer Braid and Laura S. Bruckman and Roger French and Yinghui Wu},
journal= {arXiv preprint arXiv:2302.10860},
year = {2023}
}
Comments
ACM SIGMOD Conference on Management of Data (SIGMOD)