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2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series

Author

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  • Antonello Rosato

    (Department of Information Engineering, Electronics and Telecommunications, University of Rome “La Sapienza”, Via Eudossiana 18, 00184 Rome, Italy)

  • Rodolfo Araneo

    (Electrical Engineering Division of DIAEE, University of Rome “La Sapienza”, Via Eudossiana 18, 00184 Rome, Italy)

  • Amedeo Andreotti

    (Electrical Engineering Department, University Federico II of Napoli, 80125 Napoli, Italy)

  • Federico Succetti

    (Department of Information Engineering, Electronics and Telecommunications, University of Rome “La Sapienza”, Via Eudossiana 18, 00184 Rome, Italy)

  • Massimo Panella

    (Department of Information Engineering, Electronics and Telecommunications, University of Rome “La Sapienza”, Via Eudossiana 18, 00184 Rome, Italy)

Abstract

Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a fashion that inter-dependencies among several different time series can be exploited and used for forecasting purposes by filtering and joining their samples. The resulting learning scheme can be summarized as a superposition of network layers, resulting in a stacked deep neural architecture. We proved the accuracy and robustness of the proposed approach by testing it on real-world energy problems.

Suggested Citation

  • Antonello Rosato & Rodolfo Araneo & Amedeo Andreotti & Federico Succetti & Massimo Panella, 2021. "2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series," Energies, MDPI, vol. 14(9), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2392-:d:541782
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    2. Tovar Rosas, Mario A. & Pérez, Miguel Robles & Martínez Pérez, E. Rafael, 2022. "Itineraries for charging and discharging a BESS using energy predictions based on a CNN-LSTM neural network model in BCS, Mexico," Renewable Energy, Elsevier, vol. 188(C), pages 1141-1165.

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