Abstract
In this work, we evaluate the capabilities of Deep Learning and Adversarial Networks to nowcast and discriminate the output power generation in photovoltaic systems. From a baseline spatiotemporal model of Convolutional Neural Networks and Long Short-Term Memories, we develop a discriminator and generator based on Conditional Generative Adversarial Networks. The adversarial network develops the estimation and discrimination of erroneous output power generation. Two real-world datasets are evaluated with encouraging results that are straightforwardly related to maintenance deployment applications in photovoltaic systems.
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Acknowledgements
This contribution has been supported by the Cátedra ELAND for Renewable Energies of the University of Jaén, by the Spanish government through the project RTI2018-098979-A-I00.
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Polo-Rodriguez, A., Almonacid-Olleros, G., Almonacid, G., Nugent, C., Medina-Quero, J. (2023). Discriminating Deceptive Energy Generation of Photovoltaic Systems by Deep Learning and Adversarial Networks. In: Bravo, J., Urzáiz, G. (eds) Proceedings of the 15th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2023). UCAmI 2023. Lecture Notes in Networks and Systems, vol 841. Springer, Cham. https://doi.org/10.1007/978-3-031-48590-9_7
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