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Opera.DL: Deep Learning Modelling for Photovoltaic System Monitoring

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Departament of Electronic Engineering, Campus Las Lagunillas, 23071 Jaén, Spain
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Departament of Computer Science, Campus Las Lagunillas, 23071 Jaén, Spain
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Authors to whom correspondence should be addressed.
Presented at the 13th International Conference on Ubiquitous Computing and Ambient Intelligence UCAmI 2019, Toledo, Spain, 2–5 December 2019.
Proceedings 2019, 31(1), 50; https://doi.org/10.3390/proceedings2019031050
Published: 20 November 2019
In this paper we present Deep Learning (DL) modelling to forecast the behaviour and energy production of a photovoltaic (PV) system. Using deep learning models rather than following the classical way (analytical models of PV systems) presents an outstanding advantage: context-aware learning for PV systems, which is independent of the deployment and configuration parameters of the PV system, its location and environmental conditions. These deep learning models were developed within the Ópera Digital Platform using the data of the UniVer Project, which is a standard PV system that was in place for the last twenty years in the Campus of the University of Jaén (Spain). From the obtained results, we conclude that the combination of CNN and LSTM is an encouraging model to forecast the behaviour of PV systems, even improving the results from the standard analytical model.
Keywords: photovoltaic systems monitoring; forecasting performance; deep learning models photovoltaic systems monitoring; forecasting performance; deep learning models
MDPI and ACS Style

Almonacid-Olleros, G.; Almonacid, G.; Fernandez-Carrasco, J.I.; Quero, J.M. Opera.DL: Deep Learning Modelling for Photovoltaic System Monitoring. Proceedings 2019, 31, 50.

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