Kyocera Professor, Materials Science, Case Western Reserve University. Director, MDS3 DOE-NNSA COE, SDLE Res. Cntr. Fac. Director, Applied Data Science program
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ALT Photo of a variety of people gathered around round tables in a conference room, watching a group sitting on a stage with screens that say "National Labs Day" behind them
ALT 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 reliability assessment of PV fleets. Nevertheless, the performance of PV data analysis depends on the quality of PV timeseries data. We propose a novel SpatioTemporal 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. It is empowered by two modules. (1) To cope with sparse yet various scenarios of missing data, STD-GAE incorporates a domain-knowledge aware data augmentation module to create plausible variations of missing data patterns. This generalizes STD-GAE to robust imputation over different seasons and environment. (2) STD-GAE nontrivially integrates spatiotemporal graph convolution layers and denoising autoencoder to improve the accuracy of imputation accuracy at P
ALT A line of related fuzzy monsters that ranges from more distant relatives (toward the left) to the current generation on the right. Several more recent generations are labeled, from “ME” on the far right, then “MY PARENT”, “MY GRANDPARENT”, “MY GREAT-GRANDPARENT” and “MY GREAT-GREAT GRANDPARENT” moving toward the left. The current generation monster on the left is saying “Hello! Meet some of my ancestors.” Stylized text at the top reads “intro to the autocorrelation function (ACF).”
