SPLTRAK Abstract Submission
FAIRification, Quality Assessment, and Missingness Pattern Discovery for Spatiotemporal Photovoltaic Data
William C Oltjen1, Yangxin Fan1, Jiqi Liu1, Liangyi Huang1, Mengjie Li2, Hubert Seigneur3, Xusheng Xiao1, Kristopher O Davis2, Laura S Bruckman1, Yinghui Wu1, Roger H French1
1Case Western Reserve University, Cleveland, OH, United States
/2University of Central Florida, Orlando, FL, United States
/3Florida Solar Energy Center, Cocoa, FL, United States

The growth of the photovoltaic market has pushed the demand for power forecasting and performance evaluation for a huge population of PV power plants. Many of these power plants have spatiotemporal coherence that can be utilized for improving model accuracy. We have demonstrated in this paper the FAIRification of spatiotemporal PV time series data. Through the creation of a solar power plant ontology, we propose standards for the naming and structure of metadata used to describe the data from these power plants. Using the structure from this ontology, we have developed both R and Python packages for the automation of the FAIRification process. Going further, we have also developed an R package that automates the analysis of the quality of a data set through the designation of letter grades. To solve the issue of data missingness, we propose the use of St-GNN autoencoders to detect and impute missing values from a data set by utilizing data from power plants nearby.