Recent Advancement in Geoinformatics and Data Science
CONTAINS OPEN ACCESS
Geoscience is now facing the huge potential enabled by the cyberinfrastructure, sensor network, big data, cloud computing, and data science. In this new era, what skills should geoscientists know and what actions can they take to foster new research topics? Are there already successful stories of data science in geosciences and what are the experiences? Can data science bring fresh ideas to geosciences, and vice versa? The chapters in this Special Paper present the latest progress and discoveries in both the methodology and technology of geoinformatics, and provide answers to those questions. The presented methodologies, technologies, and best practices will make this volume a useful reference with long-term impacts for data-intensive geoscience in the next decade and beyond.
A scalable solution for running ensemble simulations for photovoltaic energy
Published:March 22, 2023
Weiming Hu, Guido Cervone, Matteo Turilli, Andre Merzky, Shantenu Jha, 2023. "A scalable solution for running ensemble simulations for photovoltaic energy", Recent Advancement in Geoinformatics and Data Science, Xiaogang Ma, Matty Mookerjee, Leslie Hsu, Denise Hills
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This chapter provides an in-depth discussion of a scalable solution for running solar energy production ensemble simulations. Generating a forecast ensemble is computationally expensive. But with the help of Analog Ensemble, forecast ensembles can be generated with a single deterministic run of a weather forecast model. Weather ensembles are then used to simulate 11 10 KW photovoltaic solar power systems to study the simulation uncertainty under a wide range of panel configurations and weather conditions. This workflow has been developed and tested at scale on the National Center for Atmospheric Research supercomputer, Cheyenne, with more than 7000 concurrent cores.
Results show that spring and summer are typically associated with greater simulation uncertainty. Optimizing the panel configuration based on the individual performance of simulations under changing weather conditions can improve the accuracy of simulations by more than 12%. This work also shows how panel configuration can be optimized based on geographic locations.