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Integration of Renewable Energies---AI-Based Prediction Methods for Electricity Generation from Photovoltaic Systems

Boris Brandherm; Matthieu Deru; Alassane Ndiaye; Gian-Luca Kiefer; Jörg Baus; Ralf Gampfer
In: Thomas Barton; Christian Müller. Apply Data Science: Introduction, Applications and Projects. Pages 137-158, ISBN 978-3-658-38798-3, Springer Fachmedien, Wiesbaden, 2023.


In Germany, today's transmission grid topology often still corresponds to the technical state of the art of one-way energy flow from central power plants to consumers. The energy transition with the phase-out of nuclear power plants by 2022 and the expansion of renewable energies on the distribution grid level confronts low-voltage grids with challenges such as unmonitored overloads or voltage range violations. In addition, the volatility of loads and renewables makes it challenging to predict future grid conditions, plan preventive measures, and apply them when needed. This chapter presents the integration of renewable energies using photovoltaic systems as an example. The integration steps include the necessary information technology steps, such as collecting data from the photovoltaic system, their forwarding, preprocessing, and storage in a database, and their further processing by other services. As services, we will showcase (a) AI-based prediction methods that predict the power generation of a photovoltaic system and (b) downstream services that use these predictions for other purposes. Downstream services could be, e.g., a power grid calculation to predict future grid conditions or to provide price information for the electricity at a charging station.


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